Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore Center for Intelligent Sensors and MEMS, National University of Singapore, Singapore, 117608, Singapore Hybrid-Integrated Flexible (Stretchable) Electronic Systems Program, National University of Singapore, Singapore, 117608, Singapore NUS Suzhou Research Institute (NUSRI), Suzhou, 215123, China NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 117456, Singapore

Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

A comprehensive review on convolutional neural network in machine fault diagnosis

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A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and unmeasurable reputation loss. Existing traditional maintenance approaches suffer from some assumptions and limits, such as high prevent/repair costs, inadequate or inaccurate mathematical degradation processes and manual feature extraction. With the trend of smart manufacturing and development of Internet of Things (IoT), data mining and Artificial Intelligence (AI), etc., PdM is proposed as a novel type of maintenance paradigm to perform maintenances only after the analytical models predict certain failures or degradations. In this survey, we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multi-objective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches. Finally, important future research directions are introduced.

A Survey of Predictive Maintenance: Systems, Purposes and Approaches.

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.

https://www.mdpi.com/1424-8220/19/12/2750/pdf

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

Effective fault diagnosis of rotating machinery plays a pretty important role in the enhanced reliability and improved safety of industrial informatics applications. Although traditional intelligent fault diagnosis techniques, such as support vector machine, extreme learning machine, and convolutional neural network, might achieve satisfactory accuracy, a very high price is caused by marking all samples manually. In this article, a novel fault diagnosis method of the rotating machinery is proposed by integrating semisupervised generative adversarial nets with wavelet transform (WT-SSGANs). The proposed WT-SSGANs’ method involves two parts. In the first part, WT is adopted to transform 1-D raw vibration signals into 2-D time–frequency images. In the second part, the 2-D time–frequency images are inputted into the built SSGANs’ model to realize fault diagnosis with little labeled samples. The advantage of the built model is that the unlabeled samples might be made full use of through an adversarial learning mechanism. Finally, two case studies are implemented to verify the proposed method. The results indicate that it can achieve higher accuracy and use less labeled samples than the other existing methods in the literature. In addition, its performance in stability is pretty good as well. Competitive and promising results are still achieved when working conditions are changed.

Intelligent Fault Diagnosis via Semisupervised Generative Adversarial Nets and Wavelet Transform

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.

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

Fault diagnosis is very important to ensure the efficiency and reliability of rotating machinery. Traditional fault diagnosis methods often require manual feature design and extraction, which is an exhausting work in industrial applications. Convolutional neural network (CNN)-based methods have presented a great potential to automatically extract and select representative features for fault diagnosis. In this article, a novel three-step intelligent fault diagnosis method is proposed based on CNN and Bayesian Gaussian mixture (BGM) for rotating machinery. In the fault dataset construction step, multiple binary training datasets are constructed to amplify the difference between one fault mode and the others using a defined fault labeling rule. In the fault feature extraction step, multiple binary classification tasks are, respectively, implemented to learn the representative features for each fault mode through a proposed group of CNN models based on the above-obtained training datasets. In the fault diagnosis step, multiple Gaussian mixture models (GMMs) are adopted to fit the data distributions of the learned fault features, and an BGM model based on GMM parameters and Bayesian network is designed to establish a cause-and-effect relationship between corresponding parameters and fault modes. Two case studies are used to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method has a superior performance over the existing methods.

Guoqiang Li

Papers

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

Intelligent Fault Diagnosis via Semisupervised Generative Adversarial Nets and Wavelet Transform

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

Convolutional Neural Network-Based Bayesian Gaussian Mixture for Intelligent Fault Diagnosis of Rotating Machinery

Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data