Imbalanced Learning for Fault Diagnosis Problem of Rotating Machinery Based on Generative Adversarial Networks

Xie, Yiyang; Zhang, Tao

doi:10.23919/chicc.2018.8483334

Cited by 47 publications

(32 citation statements)

References 7 publications

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“…Experiments show that the A2CNN has strong faultdiscriminative and domain invariant capacity, and therefore its prediction can achieve a high accuracy even at different operating conditions. In [152], A novel approach for fault diagnosis based on deep convolution GAN (DCGAN) with imbalanced dataset is proposed. A new DCGAN model [153] with 4 convolutional layers respectively serving as the discriminator Generative Adversarial Networks, Goodfellow et al 2014 GANs!…”

Section: E Generative Adversarial Network (Gan)mentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

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Section: E Generative Adversarial Network (Gan)mentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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“…Compared with SMOTE approaches which are based on local information, GAN methods learn from the overall class distribution and have better performance than SMOTE [ 44 ]. Deep convolutional GAN (DCGAN) [ 45 ] and Wasserstein GAN (WGAN) [ 46 ] are also explored for imbalanced fault diagnosis tasks. In spite of powerful tools to generate complicated distributions, GANs are prone to mode collapse and are difficult to train.…”

Section: Related Workmentioning

confidence: 99%

Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation

Wang

Liu

Zhao

2020

Sensors

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Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.

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“…Ref. [ 18 ] proposed a deep convolutional GAN (DCGAN) model to simulate the original distribution from minority classes and generate new data to solve the data imbalance problem, improving the accuracy of fault diagnosis. In addition to the above studies, there are many studies around deep learning methods such as variational auto encoders (VAE) [ 19 ] and auto encoders (AE) [ 20 ], combined with the advantages of generative adversarial networks, aiming to improve the accuracy and authenticity of samples generated by GAN.…”

Section: Introductionmentioning

confidence: 99%

GAN-SAE based fault diagnosis method for electrically driven feed pumps

et al. 2020

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The running of high-speed electrically driven feed pump has a direct impact on the safety of personnel equipment and economic benefits of power plant, as the result, intelligent condition monitoring and fault diagnosis of electrically driven feed pump becomes an urgent need. In the practical process of electrically driven feed pump fault diagnosis, the running of the equipment is in normal state for a long time, occasionally, with faults, which makes the fault data very rare in a large number of monitoring data, and makes it difficult to extract the internal fault features behind the original time series data, When the deep learning theory is used in practice, the imbalance between the fault data and the normal data occurs in the operation data set. In order to solve the problem of data imbalance, this paper proposes a fault diagnosis method of GAN-SAE. This method first makes compensation for the imbalance of sample data based on the Generative Adversarial Network (GAN), and then uses the Stacked Auto Encoder (SAE) method to extract the signal features. By designing the fault diagnosis program, compared with only using SAE, back propagation neural networks (BP) and multi-hidden layer neural networks(MNN) method, the GAN-SAE method can offer better capability of extracting features, and the accuracy of fault diagnosis of electrically driven feed pump could be improved to 98.89%.

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Imbalanced Learning for Fault Diagnosis Problem of Rotating Machinery Based on Generative Adversarial Networks

Cited by 47 publications

References 7 publications

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation

GAN-SAE based fault diagnosis method for electrically driven feed pumps

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