Generative adversarial networks for data augmentation in machine fault diagnosis

Shao, Siyu; Wang, Pu; Yan, Ruqiang

doi:10.1016/j.compind.2019.01.001

Cited by 410 publications

(134 citation statements)

References 3 publications

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“…The two models are pitted against each other until the fake samples are indistinguishable from genuine ones. Using this framework, Shao et al built an auxiliary classifier GAN, named ACGAN, to generate artificial vibration data for fault diagnosis [208]. In cases of class imbalance, the use of GAN-based data augmentation supplemented the minor classes and improved the accuracy.…”

Section: ) Data Augmentationmentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

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Section: ) Data Augmentationmentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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“…However, those operations can hardly be used on a 1D feature vector. GAN (Goodfellow et al, 2014) have emerged to be a powerful tools to synthesize new data and have gained popularity in the generation of realistic natural images, and has also shown great potential to be a powerful data augmentation technique to synthetic image data with more variation and improve the generalizability of the machine learning algorithm (Shi et al, 2018;Lata et al, 2019;Sandfort et al, 2019;Shao et al, 2019). Therefore, we investigated the possibility of applying GAN for 1D structural brain feature augmentation for the improvement of classification performance in this study.…”

Section: Data Augmentation With Generative Adversarial Networkmentioning

confidence: 99%

Differential Diagnosis of Frontotemporal Dementia, Alzheimer's Disease, and Normal Aging Using a Multi-Scale Multi-Type Feature Generative Adversarial Deep Neural Network on Structural Magnetic Resonance Images

Popuri

et al. 2020

Front. Neurosci.

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Methods: Alzheimer's disease and Frontotemporal dementia are the first and third most common forms of dementia. Due to their similar clinical symptoms, they are easily misdiagnosed as each other even with sophisticated clinical guidelines. For disease-specific intervention and treatment, it is essential to develop a computer-aided system to improve the accuracy of their differential diagnosis. Recent advances in deep learning have delivered some of the best performance for medical image recognition tasks. However, its application to the differential diagnosis of AD and FTD pathology has not been explored. Approach: In this study, we proposed a novel deep learning based framework to distinguish between brain images of normal aging individuals and subjects with AD and FTD. Specifically, we combined the multi-scale and multi-type MRI-base image features with Generative Adversarial Network data augmentation technique to improve the differential diagnosis accuracy. Results: Each of the multi-scale, multitype, and data augmentation methods improved the ability for differential diagnosis for both AD and FTD. A 10-fold cross validation experiment performed on a large sample of 1,954 images using the proposed framework achieved a high overall accuracy of 88.28%. Conclusions: The salient contributions of this study are threefold: (1) our experiments demonstrate that the combination of multiple structural features extracted at different scales with our proposed deep neural network yields superior performance than Ma et al. FTD AD Differential Diagnosis individual features; (2) we show that the use of Generative Adversarial Network for data augmentation could further improve the discriminant ability of the network regarding challenging tasks such as differentiating dementia sub-types; (3) and finally, we show that ensemble classifier strategy could make the network more robust and stable.

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“…In order to enhance model stability and improve the quality of generated samples, Gao et al [22] proposed a data augmentation approach based on Wasserstein generative adversarial network with gradient penalty (WGAN-GP), which redesigned the loss function of WGAN [23]. Shao et al [24] employed one-dimensional convolutional neural network (1D-CNN) to construct an auxiliary classifier GAN (ACGAN) for data augmentation, where additional label information was conducive to generating the corresponding fault samples. Zhou et al [25] designed a new generator and discriminator using global optimization mechanism to generate discriminant fault samples.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks

et al. 2020

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Machinery fault diagnosis tasks have been well addressed when sufficient and abundant data are available. However, the data imbalance problem widely exists in real-world scenarios, which leads to the performance deterioration of fault diagnosis markedly. To solve this problem, we present a novel imbalanced fault diagnosis method based on the enhanced generative adversarial networks (GAN). By artificially generating fake samples, the proposed method can mitigate the loss caused by the lack of real fault data. Specifically, in order to improve the quality of generated samples, a new discriminator is designed using spectrum normalization (SN) strategy and a two timescale update rule (TTUR) method is used to stabilize the training process of GAN. Then, an enhanced Wasserstein GAN with gradient penalty is developed to generate high-quality synthetic samples for the fault samples set. Finally, a deep convolutional classifier is constructed to carry out fault classification. The performance and effectiveness of the proposed method are validated on the Case Western Reserve University bearing dataset and rolling bearing dataset acquired from our laboratory. The simulation results show that the proposed method has a superior performance than other methods for imbalanced fault diagnosis tasks. INDEX TERMS Fault diagnosis, rolling bearing, generative adversarial networks, imbalanced data, convolutional neural networks.

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Generative adversarial networks for data augmentation in machine fault diagnosis

Cited by 410 publications

References 3 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Differential Diagnosis of Frontotemporal Dementia, Alzheimer's Disease, and Normal Aging Using a Multi-Scale Multi-Type Feature Generative Adversarial Deep Neural Network on Structural Magnetic Resonance Images

Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks

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