Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

Deep Bidirectional Recurrent Neural Networks Ensemble for Remaining Useful Life Prediction of Aircraft Engine

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

Fuzzy logic optimization with regression analysis on EDM machining parameters of Si3N4-TiN ceramic composites

Electro discharge machining (EDM) is a cycle for molding tough materials and framing profound contour formed openings by warm disintegration in all sort of electrically conductive materials. The goal of the venture to be concentrating because of working parameters of EDM for machining of silicon nitride-titanium nitride in the machining qualities with copper electrode, for example input Spark on time (Son), current (Ip), Spark off time (Soff), spark gap and dielectric pressure on the metal removal rate (MRR) and Electrode Wear Rate (EWR) were analyzed. Subsequently, using Taguchi analysis of various plots like Mean effect plots, Interaction plots, and contour plots, performance characteristics are looked at in relation to multiple process factors. Fuzzy logic and Regression analysis is utilized to combine various reactions into a solitary trademark record known as the Multi Response Performance Index (MRPI).The trial and anticipated qualities were in a decent programming instrument for discovering the MRPI esteem. For numerous performance aspects, such as material removal rate, electrode wear rate and so on, the optimal process parameter combination was established using fuzzy logic analysis. The key process factors, which included spark off time and current, were found using an ANOVA based on a fuzzy algorithm. Topography on machined surface and cross-sectional view of conductive Si3N4-TiN composite and surface characteristics of machined electrode is examined by SEM analysis and identified the best hole surface and worst hole surface. Sensitivity analysis is being utilized to determine how much the input values, such as Ip, Son and Soff, will need to alter in order to get the desired, optimal result. In the complexity analysis, each constraint of the machine, composite and process is addressed. Future researches might look into various electrodes to assess geometrical tolerances including angularity, parallelism, total run out, flatness, straightness, concentricity, and line profile employing other optimization methodologies to achieve the best outcome. The findings of the confirmatory experiment have been established, indicating that it may be feasible to successfully strengthen the spark eroding technique.

A Taxonomy and Archetypes of Business Analytics in Smart Manufacturing

Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its multidisciplinary complexity, both researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field, as there is a lack of a holistic conceptualization. To address this issue, we performed an extensive structured literature review, yielding 904 relevant hits, to develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing. The taxonomy comprises the following meta-characteristics: application domain, orientation as the objective of the analysis, data origins, and analysis techniques. Collectively, they comprise eight dimensions with a total of 52 distinct characteristics. Using a cluster analysis, we found six archetypes that represent a synthesis of existing knowledge on planning, maintenance (reactive, offline, and online predictive), monitoring, and quality management. A temporal analysis highlights the push beyond predictive approaches and confirms that deep learning already dominates novel applications. Our results constitute an entry point to the field but can also serve as a reference work and a guide with which to assess the adequacy of one's own instruments.

Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments

Fault diagnosis has a great significance in preventing serious failures of rotating machinery and avoiding huge economic losses. The performance of the existing fault diagnosis approaches might be affected by two factors, i.e., the quality of fault features extracted from monitoring signals and the capability of fault diagnosis model. This paper proposes a new fault diagnosis method combined mel-frequency cepstral coefficients (MFCC) with a designed parallel multi-fusion convolutional neural network (MFCNN) Specifically, a MFCC-based feature extraction method is defined to reduce the noise components in monitoring signal of rotating machinery and extract more useful low-frequency fault information for downstream task. Furthermore, a novel MFCNN is designed to enrich the high-level features after each convolution operation by using multiple activation functions, so as to improve the quality of the obtained fault features. Meanwhile, a new parallel MFCNN is constructed by using a defined structural ensemble operation to improve its diagnostic performance in different noise environments. Two typical bearing and gearbox failure datasets are applied to evaluate the performance of the proposed fault diagnosis method. The experimental results indicate that the proposed parallel MFCNN has the better diagnostic performance than other methods.

Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

Tool wear is a key factor affecting many aspects of metal cutting machining, including surface quality, machining efficiency and tool life. As machining continues to evolve towards intelligence, hot spots and trends in tool wear-related research are also changing. However, in the current research on tool wear, there are still no recognized most effective tool wear suppression methods, signals are easily disturbed, low efficiency of signal processing methods and poor model generalization ability, etc. Therefore, a comprehensive summary and outlook of tool wear-related research is urgently needed, on the basis of which it is important to predict the hot spots and trends in tool wear research. In this paper, the current state of research on tool wear is systematically described from three aspects: tool wear mechanism, online monitoring and RUL (remaining useful life) prediction, and the shortcomings of tool wear-related research are pointed out. After an in-depth discussion, this paper also foresees the development trends of tool wear related research: (1) tool wear suppression research based on new technologies; (2) online monitoring and RUL prediction technology based on the fusion of data, features and pattern recognition; (3) intelligent, self-learning and self-regulating intelligent machining equipment that integrates multiple objectives (e.g. tool wear, chatter and remaining bearing life, etc.); (4) based on big data, the application of data-driven algorithms in tool wear mechanism, online monitoring and RUL prediction.

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction

The traditional predictive model for remaining useful life predictions cannot achieve adaptiveness, which is one of the main problems of said predictions. This paper proposes a LightGBM-based Remaining useful life (RUL) prediction method which considers the process and machining state. Firstly, a multi-information fusion strategy that can effectively reduce the model error and improve the generalization ability of the model is proposed. Secondly, a preprocessing method for improving the time precision and small-time granularity of feature extraction while avoiding dimensional explosion is proposed. Thirdly, an importance coefficient and a custom loss function related to the process and machining state are proposed. Finally, using the processing data of actual tool life cycle, through five evaluation indexes and 25 sets of contrast experiments, the superiority and effectiveness of the proposed method are verified.

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (V_B) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L₉ orthogonal design principle, the basic machining parameters cutting speed (v_c), feed rate (f) and depth of cut (a_p) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, V_B, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at v_c = 150 m/min, f = 0.09 mm/rev, a_p = 1 mm) to obtain the best results for V_B, Ra and the sensorial data, with a high success rate (82.5%).

Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network

Remaining useful life (RUL) prediction is very important for improving the availability of a system and reducing its life cycle cost. This paper proposes a deep long short-term memory (DLSTM) network-based RUL prediction method using multiple sensor time series signals. The DLSTM model fuses multi-sensor monitoring signals for accurate RUL prediction, which is able to discover the hidden long-term dependencies among sensor time series signals through deep learning structure. By grid search strategy, the network structure and parameters of the DLSTM are efficiently tuned using an adaptive moment estimation algorithm so as to realize an accurate and robust prediction. Two various turbofan engine datasets are adopted to verify the performance of the DLSTM model. The experimental results demonstrate that the DLSTM model has a competitive performance in comparison with state-of-the-arts reported in literatures and other neural network models.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

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.