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

Prediction of Technical State of Mechanical Systems Based on Interpretive Neural Network Model

A classic problem in prognostic and health management (PHM) is the prediction of the remaining useful life (RUL). However, until now, there has been no algorithm presented to achieve perfect performance in this challenge. This study implements a less explored approach: binary classification of the state of mechanical systems at a given forecast horizon. To prove the effectiveness of the proposed approach, tests were conducted on the C-MAPSS sample dataset. The obtained results demonstrate the achievement of an almost maximal performance threshold. The explainability of artificial intelligence (XAI) using the SHAP (Shapley Additive Explanations) feature contribution estimation method for classification models trained on data with and without a sliding window technique is also investigated.

Applied Machine Learning for IIoT and Smart Production-Methods to Improve Production Quality, Safety and Sustainability

Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain-namely security and safety, asset localization, quality control, maintenance-has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.

Wide Residual Relation Network-Based Intelligent Fault Diagnosis of Rotating Machines with Small Samples

Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus failure samples are very limited. To solve the problem above, a novel wide residual relation network (WRRN) is proposed for intelligent fault diagnosis of the RMs. Specifically, the WRRN is trained by performing a series of learning tasks in RMs with sufficient samples to obtain knowledge about how to diagnose, and then it is directly transferred to realize fault task of the RM with small samples. In this method, a wide residual network-based feature extraction module is used to generate representative fault features from input samples, and a relation module is designed to calculate the relation score between the sample pairs so as to determine their categories. Extensive experiments are conducted on two RMs to validate the WRRN method. The results demonstrate that the WRRN can accurately identify the fault types of the RMs with only small samples or even one sample. The WRRN significantly outperforms the existing popular methods in diagnostic performance.

Using Machine-Learning for the Damage Detection of Harbour Structures

The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose scanning the above and below-water port structure with a multi-sensor system, and by a fully automated process to classify the point cloud obtained into damaged and undamaged zones. We make use of simulated training data to test our approach because not enough training data with corresponding class labels are available yet. Accordingly, we build a rasterised height field of a point cloud of a sheet pile wall by subtracting a computer-aided design model. The latter is propagated through a convolutional neural network, which detects anomalies. We make use of two methods: the VGG19 deep neural network and local outlier factors. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection, which can analyse the whole structure instead of the sample-wise manual method with divers. We were able to achieve valuable results for our application. The accuracy of the proposed method is 98.8% following a desired recall of 95%. The proposed strategy is also applicable to other infrastructure objects, such as bridges and high-rise buildings.

Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples

Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled samples are required to support the algorithm of CNNs, and, in the case of a limited amount of labeled samples, this may lead to overfitting. In this article, a novel ResNet-based method is developed to achieve fault diagnoses for machines with very few samples. To be specific, data transformation combinations (DTCs) are designed based on mutual information. It is worth noting that the selected DTC, which can complete the training process of the 1-D ResNet quickly without increasing the amount of training data, can be randomly used for any batch training data. Meanwhile, a self-supervised learning method called 1-D SimCLR is adopted to obtain an effective feature encoder, which can be optimized with very few unlabeled samples. Then, a fault diagnosis model named DTC-SimCLR is constructed by combining the selected data transformation combination, the obtained feature encoder and a fully-connected layer-based classifier. In DTC-SimCLR, the parameters of the feature encoder are fixed, and the classifier is trained with very few labeled samples. Two machine fault datasets from a cutting tooth and a bearing are conducted to evaluate the performance of DTC-SimCLR. Testing results show that DTC-SimCLR has superior performance and diagnostic accuracy with very few samples.

Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network

With the continuous development of artificial intelligence technology, the value of massive power data has been widely considered. Aiming at the problem of single-phase-to-ground fault line selection in resonant grounding system, a fault line selection method based on transfer learning depthwise separable convolutional neural network (DSCNN) is proposed. The proposed method uses two pixel-level image fusions to transform the three-phase current of each feeder into the RGB color image, which is used as the input of DSCNN. After DSCNN self-feature extraction, the fault line selection is completed. With the consideration that not all of power distribution systems can obtain a large amount of data in practical applications, the transfer learning strategy is adopted to transplant the trained line selection model. The smaller number of DSCNN parameters increases the portability of the model. The test results show that not only does the proposed method extracts obvious features, but also the line selection accuracy can reach 99.76%. It also has good adaptability under different sampling frequencies, different noise environments, and different distribution network topologies; the line selection accuracy can reach more than 97.43%.

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data.

A Non-Contact Fault Diagnosis Method for Bearings and Gears Based on Generalized Matrix Norm Sparse Filtering

Fault diagnosis of mechanical equipment is mainly based on the contact measurement and analysis of vibration signals. In some special working conditions, the non-contact fault diagnosis method represented by the measurement of acoustic signals can make up for the lack of contact testing. However, its engineering application value is greatly restricted due to the low signal-to-noise ratio (SNR) of the acoustic signal. To solve this deficiency, a novel fault diagnosis method based on the generalized matrix norm sparse filtering (GMNSF) is proposed in this paper. Specially, the generalized matrix norm is introduced into the sparse filtering to seek the optimal sparse feature distribution to overcome the defect of low SNR of acoustic signals. Firstly, the collected acoustic signals are randomly overlapped to form the sample fragment data set. Then, three constraints are imposed on the multi-period data set by the GMNSF model to extract the sparse features in the sample. Finally, softmax is used to as a classifier to categorize different fault types. The diagnostic performance of the proposed method is verified by the bearing and planetary gear datasets. Results show that the GMNSF model has good feature extraction ability performance and anti-noise ability than other traditional methods.

Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing

Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotating machines, so many artificial intelligent models have been developed to diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating conditions such as experimental environment, number of balls, degree of defects, and rotational speed. Cross-domain fault diagnosis has been successfully performed when the systems are the same but operating conditions are different. However, the results are poor when diagnosing different bearing systems because the characteristics of the signals such as specific frequencies depend on the specifications. In this paper, the pre-processing method was used for improving the diagnosis without prior knowledge such as fault frequencies. The signals were first transformed to a common pattern space before entering the models. To develop and to validate the proposed method for different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve University datasets and Paderborn University datasets) were used. One dimensional CNN models were utilized for verification of the proposed method and the results of the models using raw datasets and pre-processed datasets were compared. Even though each of the ball-bearing systems have their own specifications, using the proposed method was very helpful for domain adaptation, and cross-domain fault diagnosis was performed with high accuracy.

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series

In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization.

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.

Fault Diagnosis in the Slip-Frequency Plane of Induction Machines Working in Time-Varying Conditions

Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

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.

Fault diagnosis in a current sensor and its application to fault-tolerant control for an air supply subsystem of a 50 kW-Grade fuel cell engine

The safety, reliability and stability of air supply subsystems are still problems for the commercial applications of fuel cells; therefore, engine fault diagnosis and fault-tolerant control are essential to protect the fuel cell stack. In this study, a fault diagnosis and fault-tolerant control method based on artificial neural networks (ANNs) has been proposed. The offline ANN modification model was trained with a Levenberg–Marquardt (LM) algorithm based on other sensors' signals relevant to the current sensor of a 50 kW-grade fuel cell engine test bench. The output current was predicted via the ANN identification model according to other relevant sensors and compared with the sampled current sensor signal. The faults in the current sensor were detected immediately once the difference exceeded the given threshold value, and the invalid signals of the current sensor were substituted with the predictive output value of the ANN identification model. Finally, the reconstructed current sensor signals were sent back to a fuel cell controller unit (FCU) to adjust the air flow and rotate speeds of the air compressor. Experimental results show that the typical faults in the current sensor can be diagnosed and distinguished within 0.5 s when the threshold value is 15 A. The invalid signal of current sensor can be reconstructed within 0.1 s. Which ensures that the air compressor operate normally and avoids oxygen starvation. The proposed method can protect the fuel cell stack and enhance the fault-tolerant performance of air supply subsystem used in the fuel cell engine, and it is promising to be utilized in the fault diagnosis and fault-tolerant control of various fuel cell engines and multiple sensor systems.

A fault diagnosis and fault-tolerant control method based on artificial neural networks is proposed. Faults were detected immediately once the difference exceeded the set threshold, and the invalid signals were substituted with the predictive output value.