Factors Influencing Noise Following Primary Ceramic-on-Ceramic Total Hip Arthroplasty

BACKGROUND

The noise associated with ceramic-on-ceramic (CoC) total hip arthroplasty (THA) has been a concerning issue, while its underlying causes remain unclear.

METHODS

We conducted a retrospective analysis of 119 patients (174 primary CoC THAs) who had a mean follow-up of 28 months (range, 12 to 106). A questionnaire was designed to collect information on nature, frequency, onset, duration, and impact of the noise. Postoperative x-rays were evaluated. Clinical evaluations, including Harris and Oxford hip scores, were documented at follow-up time points (6 weeks, 3 months, 6 months, and 1 year).

RESULTS

Of the 174 hips, 31.6% reported noise, including 26 popping (14.9%), 24 clicking (12.1%), and 5 grinding (2.9%). No patients reported squeaking. Noisy hips had lower age (P = .009) and body mass index (P = .019). Among patients with developmental dysplasia of the hip, 17 of 55 hips reported noise associated with smaller cup anteversion angle (P = .004), greater body height (P = .022), and larger acetabular cup size (P = .049). Noise typically began at a mean of 193 days (range, 1 to 2,598) after surgery and disappeared spontaneously in 50.9% of hips before final follow-up, with an average disappearance time of 211 days (range, 60 to 730). Noise did not affect daily life in 74.5% of patients, while 26.9% of patients who had popping reported painful sensations. One patient experienced joint dislocation, and another experienced a ceramic liner fracture during follow-up. No statistical difference was observed in outcome scores between noise and silent groups at 4 follow-up time points.

CONCLUSIONS

Incidence of noise after primary CoC THA is relatively high. Smaller cup anteversion and larger acetabular cup size were associated with noise production in patients who had developmental dysplasia of the hip.

Early Fault Detection of Rolling Bearings Based on Time-Varying Filtering Empirical Mode Decomposition and Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted

Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault characteristics. To solve this problem, a new method for early fault detection of rolling bearings is proposed, which combines multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with parameter optimization and TVFEMD. Firstly, a new weighted envelope spectrum kurtosis index is constructed using the correlation coefficient and envelope spectrum kurtosis, which is used to identify the effective component and noise component of the bearing fault signal decomposed by TVFEMD, and the intrinsic mode function (IMF) containing rich fault information is selected for reconstruction. Then, a new synthetic impact index (SII) is constructed by combining the maximum value of the autocorrelation function and the kurtosis of the envelope spectrum. The SII index is used as the fitness function of the gray wolf optimization algorithm to optimize the fault period, T, and the filter length, L, of MOMDEA. The signal reconstructed by TVF-EMD undergoes adaptive filtering using the MOMEDA method after parameter optimization. Finally, an envelope spectrum analysis is performed on the signal filtered by the adaptive MOMEDA method to extract fault feature information. The experimental results of the simulated and measured signals indicate that this method can effectively extract early fault features of rolling bearings and has good reliability. Compared to the classical FSK, MCKD, and TVFEMD-MOMEDA methods, the first-order correlated kurtosis (FCK) and fault feature coefficient (FFC) of the filtered signal obtained using the proposed method are the largest, while the sample entropy (SE) and envelope spectrum entropy (ESE) are the smallest.

Development and Production of Artificial Test Swarf to Examine Wear Behavior of Running Engine Components-Part 2: Experimentally Derived Designs

In subtractive manufacturing processes, swarf, burrs or other residues are produced, which can impair the function of a tribological system (e.g., journal bearings). To prevent premature engine damage, cleanliness requirements are defined for production processes. Damaging particle tests are an experimental approach for validating these defined cleanliness requirements. This methodical approach is not yet widely used. For one, the test setup must be developed and proven for the respective application. For another, in order to carry out the tests in a systematic manner, defined test particles with properties similar to those of the contaminants encountered in reality are required. In the second part of the paper, the process chain for manufacturing artificial test swarf by micro powder injection molding (MicroPIM) is described. The size and shape of the swarf were derived from real swarf via several abstraction processes. Although certain design guidelines for MicroPIM parts could not be taken into account, the targeted manufacturing tolerances were achieved in most cases. During demolding, it became apparent that the higher ejection forces of the free-formed geometries must be taken more into account in the design of the mold. The experiments on the test setup also revealed that the artificial test swarf was unexpectedly brittle and was therefore ground up in the bearing gap without causing any substantial damage to the bearing. Thus, the artificial test swarf in its current sintered state is not a suitable substitute for micromilled swarf. However, MicroPIM could still be used to manufacture test particles in applications involving lower mechanical forces.

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment's smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.

An Anti-Noise Convolutional Neural Network for Bearing Fault Diagnosis Based on Multi-Channel Data

In real world industrial applications, the working environment of a bearing varies with time, and some unexpected vibration noises from other equipment are inevitable. In order to improve the anti-noise performance of neural networks, a new prediction model and a multi-channel sample generation method are proposed to address the above problem. First, we proposed a multi-channel sample representation method based on the envelope time-frequency spectrum of a different channel and subsequent three-dimensional filtering to extract the fault features of samples. Second, we proposed a multi-channel data fusion neural network (MCFNN) for bearing fault discrimination, where the dropout technique is used in the training process based on a dataset with a wide rotation speed and various loads. In a noise-free environment, our experimental results demonstrated that the proposed method can reach a higher fault classification of 99.00%. In a noisy environment, the experimental results show that for the signal-to-noise ratio (SNR) of 0 dB, the fault classification averaged 11.80% higher than other methods and 32.89% higher under a SNR of -4 dB.

An Analysis of the Influence of Surface Roughness and Clearance on the Dynamic Behavior of Deep Groove Ball Bearings Using Artificial Neural Networks

The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and sorting them into classes according to the vibration level. In this paper, based on experimental research, models are created to predict the vibration class and analyze the dynamic behavior of new ball bearings. The models are based on artificial neural networks. A feedforward multilayer perceptron (MLP) was applied, and a backpropagation learning algorithm was used. A specific method of training groups of artificial neural networks was applied, where each network provided an answer to the input within the group, and the final answer was the mean value of the answers of all networks in the group. The models achieved a prediction accuracy of over 90%. The main aim of the research was to construct models that are able to predict the vibration class of a new ball bearing based on the geometric parameters of the bearing rings. The models are also applied to analyze the influence of surface roughness of the raceways and the internal radial clearance on bearing vibrations.

Higher risk of 2-year cup revision of ceramic-on-ceramic versus ceramic-on-polyethylene bearing: analysis of 33,454 primary press-fit total hip arthroplasties registered in the Dutch Arthroplasty Register (LROI)

BACKGROUND AND PURPOSE

The influence of bearing on short-term revision in press-fit total hip arthroplasty (THA) remains under-reported. The aim of this study was to describe 2-year cup revision rates of ceramic-on-ceramic (CoC) and ceramic-on-polyethylene (CoPE).

PATIENTS AND METHODS

Primary press-fit THAs with one of the three most used cups available with both CoC or CoPE bearing recorded in the Dutch Arthroplasty Register (LROI) were included (2007-2019). Primary outcome was 2-year cup revision for all reasons. Secondary outcomes were: reasons for revision, incidence of different revision procedures and use of both bearings over time.

RESULTS

2-year Kaplan-Meier cup revision rate in 33,454 THAs (12,535 CoC; 20,919 CoPE) showed a higher rate in CoC (0.67% [95% CI, 0.54-0.81]) compared to CoPE (0.44% [95% CI, 0.34-0.54]) (p = 0.004). Correction for confounders (age, gender, cup type, head size) resulted in a hazard ratio (HR) of 0.64 [95%CI, 0.48-0.87] (p = 0.019). Reasons for cup revision differed only by more cup revision due to loosening in CoC (26.2% vs.1 3.2%) (p = 0.030). For aseptic loosening a revision rate of 0.153% [95% CI, 0.075-0.231] was seen in CoC and 0.058% [95%CI 0.019-0.097] in CoPE (p = 0.007). Correction for head size resulted in a HR of 0.475 [95% CI, 0.197-1.141] (p = 0.096). Incidence of different revision procedures did not differ between bearings. Over time the use of CoPE has increased and CoC decreased.

CONCLUSIONS

A higher 2-year cup revision rate in press-fit THA was observed in CoC compared to CoPE. Cup loosening was the only significantly different reason for revision and seen more often in CoC and mostly aseptic. Future randomised controlled trials need to confirm causality, since the early cup revision data provided has the potential to be useful when choosing the bearing in press-fit THA, when combined with other factors like bone quality and patient and implant characteristics.

Bearing Fault Diagnosis Using a Hybrid Fuzzy V-Structure Fault Estimator Scheme

Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size identification in the bearing using vibration signals. The estimator was designed based on the combination of a fuzzy algorithm and a V-structure approach to reduce the oscillation and improve the unknown condition's estimation and prediction in using the V-structure method. The V-structure surface is developed by the proposed fuzzy algorithm, which reduces the vibrations and improves the stability. In addition, the parallel fuzzy method is used to improve the robustness and stability of the V-structure algorithm. For data modeling, the proposed combination of an external autoregression error, a Laguerre filter, and a support vector regression algorithm was employed. Finally, the support vector machine algorithm was used for data classification and crack size detection. The effectiveness of the proposed approach was evaluated by leveraging the vibration signals provided in the Case Western Reserve University bearing dataset. The dataset consists of four conditions: normal, ball failure, inner fault, and outer fault. The results showed that the average accuracy of fault classification and crack size identification using the hybrid fuzzy V-structure fuzzy fault estimation algorithm was 98.75% and 98%, respectively.

Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

Do's and Don'ts in Primary Aneurysmal Bone Cysts of the Proximal Femur in Children and Adolescents: Retrospective Multicenter EPOS Study of 79 Patients

BACKGROUND

Aneurysmal bone cysts (ABC) are rare benign cystic bone tumors, generally diagnosed in children and adolescents. Proximal femoral ABCs may require specific treatment strategies because of an increased pathologic fracture risk. As few reports are published on ABCs, specifically for this localization, consensus regarding optimal treatment is lacking. We present a large retrospective study on the treatment of pediatric proximal femoral ABCs.

METHODS

All eligible pediatric patients with proximal femoral ABC were included, from 11 tertiary referral centers for musculo-skeletal oncology (2000-2021). Patient demographics, diagnostics, treatments, and complications were evaluated. Index procedures were categorized as percutaneous/open procedures and osteosynthesis alone. Primary outcomes were: time until full weight-bearing and failure-free survival. Failure was defined as open procedure after primary surgery, >3 percutaneous procedures, recurrence, and/or fracture. Risk factors for failure were evaluated.

RESULTS

Seventy-nine patients with ABC were included [mean age, 10.2 (±SD4.0) y, n=56 male]. The median follow-up was 5.1 years (interquartile ranges=2.5 to 8.8).Index procedure was percutaneous procedure (n=22), open procedure (n=35), or osteosynthesis alone (n=22). The median time until full weight-bearing was 13 weeks [95% confidence interval (CI)=7.9-18.1] for open procedures, 9 weeks (95% CI=1.4-16.6) for percutaneous, and 6 weeks (95% CI=4.3-7.7) for osteosynthesis alone ( P =0.1). Failure rates were 41%, 43%, and 36%, respectively. Overall, 2 and 5-year failure-free survival was 69.6% (95% CI=59.2-80.0) and 54.5% (95% CI=41.6-67.4), respectively. Risk factors associated with failure were age younger than 10 years [hazard ratios (HR)=2.9, 95% CI=1.4-5.8], cyst volume >55 cm 3 (HR=1.7, 95% CI=0.8-2.5), and fracture at diagnosis (HR=1.4, 95% CI=0.7-3.3).

CONCLUSIONS

As both open and percutaneous procedures along with osteosynthesis alone seem viable treatment options in this weight-bearing location, optimal treatment for proximal femoral ABCs remains unclear. The aim of the treatment was to achieve local cyst control while minimizing complications and ensuring that children can continue their normal activities as soon as possible. A personalized balance should be maintained between undertreatment, with potentially higher risks of pathologic fractures, prolonged periods of partial weight-bearing, or recurrences, versus overtreatment with large surgical procedures, and associated risks.

LEVEL OF EVIDENCE

Level IV, therapeutic study.

Bearing fault diagnosis based on particle swarm optimization fusion convolutional neural network

Bearings are the most basic and important mechanical parts. The stable and safe operation of the equipment requires bearing fault diagnosis in advance. So, bearing fault diagnosis is an important technology. However, the feature extraction quality of the traditional convolutional neural network bearing fault diagnosis is not high and the recognition accuracy will decline under different working conditions. In response to these questions, a bearing fault model based on particle swarm optimization (PSO) fusion convolution neural network is proposed in this paper. The model first adaptively adjusts the hyperparameters of the model through PSO, then introduces residual connections to prevent the gradient from disappearing, uses global average pooling to replace the fully connected layer to reduce the training parameters of the model, and finally adds a dropout layer to prevent network overfitting. The experimental results show that the model is under four conditions, two of which can achieve 100% recognition, and the other two can also achieve more than 98% accuracy. And compared with the traditional diagnosis method, the model has higher accuracy under variable working conditions. This research has important research significance and economic value in the field of the intelligent machinery industry.

Bearing Fault Diagnosis Based on Randomized Fisher Discriminant Analysis

In this paper, a novel randomized Fisher discriminant analysis (RFDA) based bearing fault diagnosis method is proposed. First, several representative time-domain features are extracted from the raw vibration signals. Second, linear Fisher discriminant analysis (FDA) is extended to nonlinear FDA named RFDA by introducing the random feature map to deal with the non-linearity issue. Specifically, the extracted time-domain features data are mapped onto a high-dimensional space using the random feature map function rather than kernel functions. Third, the time-domain features are fed into the built RFDA model to extract the discriminant features for diagnosis. Moreover, a Bayesian inference is employed to identify the class of the collected vibration signals to diagnose the bearing status. The proposed method uses random Fourier features to approximate the kernel matrix in the kernel Fisher discriminant analysis. Through employing randomized Fisher discriminant analysis, the nonlinearity issue is dealt with, and the computational burden is remarkably reduced compared to the kernel Fisher discriminant analysis (KFDA). To illustrate the superior performance of the proposed RFDA-based bearing fault diagnosis method, comparative experiments are conducted on two widely used datasets, the Case Western Reserve University (CWRU) bearing dataset and the Paderborn University (PU) bearing dataset. For the CWRU dataset, the computation time of RFDA is much shorter than KFDA, while the accuracy rate reaches the same level of KFDA. For the PU dataset, the accuracy rate of RFDA is slightly higher than KFDA, and the computation time is only 44.14% of KFDA.

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

Deep Learning-Based Remaining Useful Life Estimation of Bearings with Time-Frequency Information

In modern industrial production, the prediction ability of remaining useful life of bearings directly affects the safety and stability of the system. Traditional methods require rigorous physical modeling and perform poorly for complex systems. In this paper, an end-to-end remaining useful life prediction method is proposed, which uses short-time Fourier transform (STFT) as preprocessing. Considering the time correlation of signal sequences, a long and short-term memory network is designed in CNN, incorporating the convolutional block attention module, and understanding the decision-making process of the network from the interpretability level. Experiments were carried out on the 2012PHM dataset and compared with other methods, and the results proved the effectiveness of the method.

Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA

In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual information (EEMI) was introduced to comprehensively consider the energy distribution of the modes and the dependence between the modes and the original signal, and the EEMI was used as the objective function. In addition, TVF-EMD was optimized by IGOA and the optimal parameters matching the input signal were obtained. Finally, the feature frequency of the signal was extracted by analyzing the sensitive mode with larger kurtosis. The optimization experiments of 23 sets of benchmark functions showed that IGOA not only enhanced the balance between exploration and development, but also improved the global and local search ability and stability of the algorithm. The analysis of the simulation signal and bearing signal shows that the parameter-adaptive TVF-EMD method can separate the modes with specific physical meanings accurately. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), TVF-EMD with fixed parameters and GOA-TVF-EMD, the decomposition performance of the proposed method is better. The proposed method not only improved the under-decomposition, over-decomposition and modal aliasing problems of TVF-EMD, but could also accurately separate the frequency components of the signal and extract the included feature information, so it has practical significance in mechanical fault diagnosis.

Combine Harvester Bearing Fault-Diagnosis Method Based on SDAE-RCmvMSE

In the fault monitoring of rolling bearings, there is always loud noise, leading to poor signal stationariness. How to accurately and efficiently identify the fault type of rolling bearings is a challenge. Based on multivariate multiscale sample entropy (mvMSE), this paper introduces the refined composite mvMSE (RCmvMSE) into the fault extraction of the rolling bearing. A rolling bearing fault-diagnosis method based on stacked auto encoder and RCmvMSE (SDAE-RCmvMSE) is proposed. In the actual environment, the fault-diagnosis method use the multichannel vibration signals of the bearing as the input of stacked denoising autoencoders (SDAEs) to filter the noise of the vibration signals. The features of denoise signals are extracted by RCmvMSE and the rolling bearing operation-state diagnosis is completed with a support-vector machine (SVM) model. The results show that in the original test data, the accuracy rates of SDAE-RCmvMSE, RCmvMSE, and commonplace features of vibration signals combined with SVM (CFVS-SVM) methods are 99.5%, 100%, and 96% respectively. In the data with noise, the accuracy rates of RCmvMSE and CFVS-SVM are 97.75% and 93.08%, respectively, but the accuracy of SDAE-RCmvMSE is still 100%.

A Postmortem Analysis of Polyethylene Damage and Periprosthetic Tissue in Rotating Platform and Fixed Bearing Tibial Inserts

BACKGROUND

Mobile bearing designs are intended to reduce wear, but mixed results were reported from retrieval analyses. Postmortem evaluation (PM) provides the opportunity to assess polyethylene damage in successful implants. We compared damage patterns, MRI presentation, and histology between mobile-bearing and fixed tibial inserts retrieved postmortem and compared these results to our prior findings from implants retrieved at revision.

METHODS

Eleven postmortem knees with rotating platform (RP) implants and 13 with fixed bearing (FB) implants were examined. All were MRI scanned, and tissue samples were collected from standardized regions for histology. Polyethylene inserts were subjectively scored to assess articular, backside, and PS post surfaces for damage modes and severity.

RESULTS

Average duration of implantation was 9.3 years (1.7-19.6 years). Surface burnishing was the most common polyethylene damage mode. Average damage scores were higher for RP (53.4) compared to FB inserts (34.4) due to greater backside damage (13.4 for RP vs 1.4 for FB). A minimal difference in damage was observed on the articular surfaces (37.4 RP vs 30.0 FB). Mild innate macrophage reactions were seen in 8 (72.7%) RP and 5 (45.5%) FB specimens. Polyethylene particles were identified in 7 (63.6%) RP and 3 (27.7%) FB specimens.

CONCLUSIONS

Postmortem inserts showed low damage levels and mild tissue reactions compared to those reported for implants removed at revision arthroplasty. Nonetheless, trends in comparing RP and FB inserts were consistent with those seen in retrieval analyses, demonstrating the usefulness of retrieval studies in capturing performance differences among TKA designs.

Online Condition Monitoring of Rotating Machines by Self-Powered Piezoelectric Transducer from Real-Time Experimental Investigations

This paper investigates self-powering online condition monitoring for rotating machines by the piezoelectric transducer as an energy harvester and sensor. The method is devised for real-time working motors and relies on self-powered wireless data transfer where the data comes from the piezoelectric transducer's output. Energy harvesting by Piezoceramic is studied under real-time motor excitations, followed by power optimization schemes. The maximum power and root mean square power generation from the motor excitation are 13.43 mW/g² and 5.9 mW/g², which can be enough for providing autonomous wireless data transfer. The piezoelectric transducer sensitivity to the fault is experimentally investigated, showing the considerable fault sensitivity of piezoelectric transducer output to the fault. For instance, the piezoelectric transducer output under a shaft-misalignment fault is more than 200% higher than the healthy working conditions. This outcome indicates that the monitoring of rotating machines can be achieved by using a self-powered system of the piezoelectric harvesters. Finally, a discussion on the feasible self-powered online condition monitoring is presented.

Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.

Effect of Glass Fiber Hybridization on the Durability in Salt-Fog Environment of Pinned Flax Composites

The aim of the present paper is to evaluate the effect of the hybridization with external layers of glass fibers on the durability of flax fiber reinforced composites in severe aging conditions. To this scope, full glass, full flax and hybrid glass–flax pinned laminates were exposed to a salt-fog environment for up to 60 days. Double-lap pinned joint tests were performed to assess the pin-hole joints performances at varying the laminate stacking sequence. In order to better discriminate the relationship between the mechanical behavior and the fracture mechanisms of joints at increasing the aging time, different geometries (i.e., by varying both the hole diameter D and the free edge distance from the center of the hole E) were investigated after 0 (i.e., unaged samples), 30 and 60 days of salt-fog exposition. It was shown that the hybridization positively affects the mechanical performance as well as the stability of pinned composites: i.e., improvements in both strength and durability against the salt-fog environment were evidenced. Indeed, the hybrid laminate exhibited a reduction in the bearing strength of about 20% after 60 days of aging, despite to full flax laminate, for which a total reduction in the bearing strength of 29% was observed. Finally, a simplified joint failure map was assessed, which clusters the main failure mechanisms observed for pinned composites at varying aging conditions, thus assisting the joining design of flax–glass hybrid laminates.

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.

Disappointing long-term outcome of THA with carbon-fiber-reinforced poly-ether-ether-ketone (CFR-PEEK) as acetabular insert liner: a prospective study with a mean follow-up of 14.3 years

INTRODUCTION

Insert liner wear of the acetabular component is one of the predictive values for survival of total hip arthroplasties (THAs). This prospective single-centre study was designed to evaluate the follow-up of carbon-fiber-reinforced poly-ether-ether-ketone (CFR-PEEK) insert liner used as bearing in cementless THAs.

METHODS

29 healthy patients with an indication for cementless THA were selected for a CFR-PEEK insert liner and followed over time. All patients received a cementless THA with a CFR-PEEK insert liner used as bearing. At different follow-up moments patients were routinely examined and were analysed using the Oxford Hip Score (OHS), the modified Merle d'Aubigne-Postel (MAP) score, and radiologically. At the follow up moments the plain radiographics where assessed for loosening, cyst formations and wear of the CFR-PEEK liners.

RESULTS

At a mean of 14.3 years follow-up 4 revisions of the acetabular component were performed, resulting in a survival rate of 86.5% (CI 95%, 72.4-96.6). A statistically significant difference in OHS and MAP scores between pre- and postoperative follow-up moments was observed. The acetabular components of the remaining patients showed no radiological abnormalities at 14.3 years follow-up. The overall CFR-PEEK wear was low, with a mean of 0.81 (0.2-1.4) mm wear at 14.3 years follow-up.

CONCLUSIONS

In this series we found an aseptic loosening with unclear reasons in 4 well-positioned acetabular components, hence we do not recommend routine use of CFR-PEEK insert liners as bearing in cementless THAs. All the remaining THAs and acetabular components were in situ without abnormalities at 14.3 years follow-up.

Ceramic Bearings Are Associated With a Significantly Reduced Revision Rate in Primary Hip Arthroplasty: An Analysis From the National Joint Registry for England, Wales, Northern Ireland, and the Isle of Man

BACKGROUND

Prosthetic joint infection (PJI) is a devastating complication. Studies have suggested reduction in PJI with the use of ceramic bearings.

METHODS

Adult patients who underwent total hip arthroplasty (THA) using an uncemented acetabular component with ceramic-on-ceramic (CoC), ceramic-on-polyethylene (CoP), or metal-on-polyethylene (MoP) bearing surfaces between 2002 and 2016 were extracted from the National Joint Registry for England, Wales, Northern Ireland, and the Isle of Man. A competing risk regression model to investigate predictors of each revision outcome was used. Time-to-event was determined by duration of implantation since primary surgery with competing risks being death or revision. The results were adjusted for age, gender, American Association of Anaesthesiologists grade, body mass index, surgical indication, intraoperative complications, and implant data.

RESULTS

In total, 456,457 THAs (228,786 MoP, 128,403 CoC, and 99,268 CoP) were identified. Multivariable modeling showed that the risk of revision for PJI was significantly lower with CoC (risk ratio 0.748, P < .001) and CoP (risk ratio 0.775, P < .001) compared to MoP. Significant reduction in risk of aseptic and all-cause revision was also seen. The significant protective effect of ceramic bearing was predominantly seen 2 years after implantation. Aseptic revision beyond 2 years reduced by 18.1% and 24.8% for CoC and CoP (P < .001), respectively. All-cause revision rate beyond 2 years reduced by 21.6% for CoC and 27.1% for CoP (P < .001) CONCLUSION: This study demonstrates an association between the use of ceramic as part of the bearing, with lower rates of revision for all causes, revision for infection, and revision for aseptic causes, supporting ceramic bearings in THA.

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.

Crack Size Identification for Bearings Using an Adaptive Digital Twin

In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.

Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.

An Explainable AI-Based Fault Diagnosis Model for Bearings

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector-Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley explanation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explainability is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorporating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demonstrated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included.

Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification

Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps. First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal under normal conditions and extract the state-space equation. After signal modeling, an adaptive neural-fuzzy structure observer is designed using a combination of high-order variable structure techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five state-of-the-art techniques. The proposed algorithm improved the average pattern recognition and crack size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of the variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model and SVM, respectively.

Ceramic-on-Ceramic Bearing in Total Hip Arthroplasty Reduces the Risk for Revision for Periprosthetic Joint Infection Compared to Ceramic-on-Polyethylene: A Matched Analysis of 118,753 Cementless THA Based on the German Arthroplasty Registry

Periprosthetic joint infection (PJI) is one of the most common complications in total hip arthroplasty (THA). The influence of bearing material on the risk of PJI remains unclear to date. This registry-based matched study investigates the role of bearing partners in primary cementless THA. Primary cementless THAs recorded in the German Arthroplasty Registry since 2012 with either a ceramic-on-ceramic (CoC) or ceramic-on-polyethylene (CoP) bearings were included in the analysis. Using propensity score matching (PSM) for age, sex, obesity, diabetes mellitus, Elixhauser comorbidity index, year of surgery and head size, we compared the risk for revision for PJI for CoC and CoP. Within the 115,538 THAs (87.1% CoP; 12.9% CoC), 977 revisions were performed due to PJI. There was a significantly higher risk for revision for PJI for CoP compared with CoC over the whole study period (p < 0.01) after 2:1 matching (CoP:CoC) with a hazard ratio of 1.41 (95% confidence interval (CI), 1.09 to 1.80) After 3 years, the risk for revision for PJI was 0.7% (CI 0.5–0.9%) for CoC and 0.9% (CI 0.8–1.1%) for CoP. The risk for revision for all other reasons except PJI did not significantly differ between the two groups over the whole study period (p = 0.4). Cementless THAs with CoC bearings were less likely to be revised because of infection in mid-term follow-up. In the future, registry-embedded studies focusing on long-term follow-up, including clinical data, as well as basic science studies, may give a deeper insight into the influence of the bearing partners.

Fault Detection Based on Multi-Dimensional KDE and Jensen-Shannon Divergence

Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on T2 statistics or cross entropy. This paper proposes a new fault diagnosis method based on optimal bandwidth kernel density estimation (KDE) and Jensen–Shannon (JS) divergence distribution for improved fault detection performance. KDE addresses weak signal and coupling fault detection, and JS divergence addresses unknown fault detection. Firstly, the formula and algorithm of the optimal bandwidth of multidimensional KDE are presented, and the convergence of the algorithm is proved. Secondly, the difference in JS divergence between the data is obtained based on the optimal KDE and used for fault detection. Finally, the fault diagnosis experiment based on the bearing data from Case Western Reserve University Bearing Data Center is conducted. The results show that for known faults, the proposed method has 10% and 2% higher detection rate than T2 statistics and the cross entropy method, respectively. For unknown faults, T2 statistics cannot effectively detect faults, and the proposed method has approximately 15% higher detection rate than the cross entropy method. Thus, the proposed method can effectively improve the fault detection rate.

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.

Influence of the Bearing Thermal Deformation on Nonlinear Dynamic Characteristics of an Electric Drive Helical Gear System

Based on the statics and quasi-statics analysis methods, the thermal deformation calculation model of a deep-groove ball bearing was constructed for the helical gear transmission system of a high speed electric drive, and the radial and axial bearing stiffness values of the bearing were calculated under the thermal deformation in this study. The obtained radial and axial stiffness values were introduced into the established dynamics model of helical gear system, and the influence of changed bearing stiffness, resulting from the thermal deformation, on the nonlinear dynamic characteristics of gear pair was analyzed using the Runge–Kutta method. The results show that the axial and radial deformations of bearing occur due to the increase of working speed and temperature, in which the axial stiffness of bearing is improved but the radial stiffness is reduced. The decreasing degree of axial stiffness and the increasing degree of radial stiffness decrease with the gradually increasing working rotational speed. When considering the influence of thermal deformation on the bearing stiffness, the helical gear system will have nonlinear behaviors, such as single periodic, double periodic, and chaotic motion with the change of working speed. Therefore, in order to improve the nonlinear dynamic characteristics of high speed electric drive gear systems, the influence of bearing stiffness change on the dynamic performance of a gear system should be considered in the industrial applications.

A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions.

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

Phase Synchrony Analysis of Rolling Bearing Vibrations and Its Application to Failure Identification

As the failure-induced component (FIC) in the vibration signals of bearings transmits through housings and shafts, potential phase synchronization is excited among multichannel signals. As phase synchrony analysis (PSA) does not involve the chaotic behavior of signals, it is suitable for characterizing the operating state of bearings considering complicated vibration signals. Therefore, a novel PSA method was developed to identify and track the failure evolution of bearings. First, resonance demodulation and variational mode decomposition (VMD) were combined to extract the mono-component or band-limited FIC from signals. Then, the instantaneous phase of the FIC was analytically solved using Hilbert transformation. The generalized phase difference (GPD) was used to quantify the relationship between FICs extracted from different vibration signals. The entropy of the GPD was regarded as the indicator for quantifying failure evolution. The proposed method was applied to the vibration signals obtained from an accelerated failure experiment and a natural failure experiment. Results showed that phase synchronization in bearing failure evolution was detected and evaluated effectively. Despite the chaotic behavior of the signals, the phase synchronization indicator could identify bearing failure during the initial stage in a robust manner.

Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

Pinned Hybrid Glass-Flax Composite Laminates Aged in Salt-Fog Environment: Mechanical Durability

The aim of the present paper is to study the mechanical performance evolution of pinned hybrid glass-flax composite laminates under environment aging conditions. Hybrid glass-flax fibers/epoxy pinned laminates were exposed to salt-spray fog environmental conditions up to 60 days. With the purpose of assessing the relationship between mechanical performances and failure mechanisms at increasing aging time, single lap joints at varying joint geometry (i.e., hole diameter D and hole distance E from free edge) were characterized after 0 days (i.e., unaged samples), 30 days, and 60 days of salt-fog exposition. Based on this approach, the property-structure relationship of the composite laminates was assessed on these critical environmental conditions. In particular, a reduction of failure strength for long-aging-time-aged samples was observed in the range 20-30% compared to unaged one. Due to the natural fiber degradation in a salt-fog environment, premature catastrophic fractures mode due to shear-out and net-tension were found, related to reduced joint fracture strength. This behavior identifies that this type of joint requires a careful design in order to guarantee an effective mechanical stability of the composite hybrid joint under long-term operating conditions in an aggressive environment.

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.

Are Ceramic Bearings Becoming Cost-Effective for All Patients Within a 90-Day Bundled Payment Period?

BACKGROUND

We analyzed whether the total hospital cost in a 90-day bundled payment period for ceramic-on-polyethylene (C-PE) and ceramic-on-ceramic (COC) total hip arthroplasty (THA) bearings was changing over time, and whether the cost differential between ceramic bearings and metal-on-polyethylene (M-PE) bearings was approaching the previously published tipping point for cost-effectiveness of US$325.

METHODS

A total of 245,077 elderly Medicare patients (65+) who underwent primary THA between 2010 and 2015 were identified from the United States Medicare 100% national administrative hospital claims database. The total inpatient cost, calculated up to 90 days after index discharge, was computed using cost-to-charge ratios, and hospital payment was analyzed. The differential total inpatient cost of C-PE and COC bearings, compared to metal-on-polyethylene (M-PE), was evaluated using parametric and nonparametric models.

RESULTS

After adjustment for patient and clinical factors, and the year of surgery, the mean hospital cost up to 90 days for primary THA with C-PE or COC was within ±1% of the cost for primary THA with M-PE bearings (P < .001). From the nonparametric analysis, the median total hospital cost was US$296-US$353 more for C-PE and COC than M-PE. Cost differentials were found to decrease significantly over time (P < .001).

CONCLUSION

Patient and clinical factors had a far greater impact on the total cost of inpatient THA surgery than bearing selection, even when including readmission costs up to 90 days after discharge. Our findings indicate that the cost-effectiveness thresholds for ceramic bearings relative to M-PE are changing over time and increasingly achievable for the Medicare population.

Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input

The bearing is a component of the support shaft that guides the rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint. In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods. Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained. Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input. Experimental results showed that the proposed method has a desirable 99.93% classification accuracy in the case of less labeled data from the public data set of West Reserve University, which is better than the state-of-the-art methods.

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TE_D = 96%, TE_CG-A = 97%, TE_CG-B = 100%).

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions.

An Intelligent Fault Diagnosis Method for Bearings with Variable Rotating Speed Based on Pythagorean Spatial Pyramid Pooling CNN

Deep learning methods have been introduced for fault diagnosis of rotating machinery. Most methods have good performance when processing bearing data at a certain rotating speed. However, most rotating machinery in industrial practice has variable working speed. When processing the bearing data with variable rotating speed, the existing methods have low accuracies, or need complex parameter adjustments. To solve this problem, a fault diagnosis method based on continuous wavelet transform scalogram (CWTS) and Pythagorean spatial pyramid pooling convolutional neural network (PSPP-CNN) is proposed in this paper. In this method, continuous wavelet transform is used to decompose vibration signals into CWTSs with different scale ranges according to the rotating speed. By adding a PSPP layer, CNN can process CWTSs in different sizes. Then the fault diagnosis of variable rotating speed bearing can be carried out by a single CNN model without complex parameter adjustment. Compared with a spatial pyramid pooling (SPP) layer that has been used in CNN, a PSPP layer locates as front layer of CNN. Thus, the features obtained by PSPP layer can be delivered to convolutional layers for further feature extraction. According to experiment results, this method has higher diagnosis accuracy for variable rotating speed bearing than other methods. In addition, the PSPP-CNN model trained by data at some rotating speeds can be used to diagnose bearing fault at full working speed.

Controlling Normal Stiffness in Droplet-Based Linear Bearings

While capillary forces are negligible relative to gravity at the macroscale, they provide adequate force to effectively manipulate millimeter to micro meter objects. The fluidic actuation can be accomplished using droplets that also act as bearings. While rotary droplet bearings have been previously demonstrated, this paper addresses the positioning accuracy of a droplet-based bearing consisting of a droplet between a moving plate and a stationary substrate with constrained wetting region under a normal load. Key wetting cases are analyzed using both closed form analytical approximations and numerical simulations. The vertical force and stiffness characteristics are analyzed in relation to the wetting boundaries of the supporting surface. Case studies of different wetting boundaries are presented and summarized. Design strategies are presented for maximizing load carrying capability and stiffness. These results show that controlled wetting and opposing droplet configurations can create much higher stiffness fluidic bearings than simple droplets.

Nanoscale Assembly of Copper Bearing-Sleeve via Cold-Welding: A Molecular Dynamics Study

A bearing is an important component in contemporary machinery and equipment, whose main function is to support the mechanical rotator, reduce the friction coefficient during its movement, and guarantee the turning accuracy. However, assembly of a nanoscale bearing and sleeve is a challenging process for micro-nano mechanical manufacturers. Hence, we show the cold-welding mechanism of a copper nanobearing-nanosleeve via molecular dynamic simulations. We demonstrate that it is feasible to assemble a bearing and sleeve at the nanoscale to form a stable mechanism. The effect of temperature in the range of 150 to 750 K is investigated. As the temperature rises, the mechanical strength and the weld stress of the welded structures markedly decrease, accompanied by the observation of increasing disorder magnitude. This comparison study is believed to facilitate future mechanical processing and structural nano-assembly of metallic elements for better mechanical performance.

Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis

Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes.

Bearings Downsizing by Strength Enhancement and Service Life Extension

Slim bearings are used widely in aircrafts, robots, wind turbines, and industrial machineries, where their size and weight are very important for the performance of a system. The common materials of slim bearings for robots and industrial machineries are based on SAE52110 bearing steel, and special heat treatment and a super polishing process are used and adapted to improve the rolling contact fatigue (RCF) strength of bearings. The improvement in RCF strength, depending on contact stress, surface hardness, and the friction behavior before and after ultrasonic nanocrystalline surface modification (UNSM) treatment was validated. Simple analysis shows that these improvements can reduce the size and weight of slim bearings down to about 3.40–21.25% and 14.3–26.05%, respectively. Hence, this UNSM technology is an opportunity to implement cost-saving and energy consuming super-polishing, a heat treatment process, and to reduce the size and weight of slim bearings.

Clinical Outcomes and Bearing-Specific Complications Following Fourth-Generation Alumina Ceramic-on-Ceramic Total Hip Arthroplasty: A Single-Surgeon Series of 749 Hips at a Minimum of 5-Year Follow-Up

BACKGROUND

The purpose of this study is to evaluate the minimum 5-year outcomes and bearing-specific complications in a single surgeon series of fourth-generation alumina ceramic-on-ceramic total hip arthroplasties (THAs).

METHODS

We retrospectively analyzed 667 patients (749 hips) who underwent primary THAs by a single surgeon using fourth-generation alumina ceramic bearings. There were 315 men and 352 women with a mean age of 54.2 years. The surgeon used cementless prostheses with an identical design and BIOLOX Delta ceramics in all hips, using a 36-mm head in 472 hips (63%) and a 32-mm head in 227. The mean follow-up duration was 6.5 years (range, 5 to 8 years).

RESULTS

The mean Harris hip score improved from 45.6 points preoperatively to 91.3 points at final follow-up. All but 1 acetabular cup and all femoral stems were well fixed. No radiographic evidence of osteolysis was identified at final follow-up. There were 2 (0.3%) ceramic liner fractures and no ceramic head fractures. A total of 48 hips (6.4%) exhibited audible noise (29 clickings and 19 squeakings), but no patient required revision. Other complications were 1 dislocation, 1 deep infection, 3 iliopsoas tendonitis, and 6 periprosthetic femoral fractures. Kaplan-Meier survivorship for revision for any reason was 98.6% (95% confidence interval, 97.7-99.5) at 6.5 years.

CONCLUSION

Delta ceramic-on-ceramic THAs had a high rate of survivorship without radiographic evidence of osteolysis at 6.5-year follow-up. However, we found 0.3% ceramic liner fractures and 6.4% audible noises associated with the use of Delta ceramics.