Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty

Gao, Xin; Deng, Fang; Yue, Xianghu

doi:10.1016/j.neucom.2018.10.109

Cited by 200 publications

(56 citation statements)

References 8 publications

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“…Therefore, previous studies have used many different data augmentation methods to solve this problem, such as using a synthetic over-sampling method to generate new samples by interpolating real samples, transforming the available data to generate additional data variants, or using different signal processing techniques to add Gaussian noise, signal translation, amplitude shifting, and time stretching [20], [21]. While the results suggest that deep learning-based fault diagnosis methods can largely benefit from an expanded dataset with more generated labeled instances, but the new samples are generally similar to the real samples and lack sufficient diversity of data, so improvement in model generalization are limited [21][22][23]. In addition, generative adversarial networks (GANs) have been recently used to learn the distributions of the machinery vibration data and generate additional realistic fake samples to expand the training dataset, despite the improved testing performance, the main drawback of GANs-based bearing fault diagnostics is its reliance on a relatively large model consisting of multiple GANs for the minority classes [24].…”

Section: Introductionmentioning

confidence: 99%

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

Lü

Han

et al. 2020

IEEE Access

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It is very important and necessary to diagnose bearing faults timely, quickly, and accurately in practical applications, because the operation status of the bearings is directly related to the performance and reliability of the rotating machinery. Therefore, a generic intelligent bearing fault diagnosis system based on AlexNet with transfer learning was proposed to automatically identify and classify different bearing faults. Transfer learning was used to avoid overfitting problem of deep network. Five bearing faults at two different motor loads and speeds were selected from the Case Western Reserve University (CWRU) bearing dataset to validate the performance of the proposed method. Results showed that compared to previous methods, the proposed method achieved excellent performance, with overall classification accuracy over 99.7%, and fast training and testing times. Feature visualization displayed the common and high-level features of spectrograms of vibration signals learned by the trained classification model. And strongest activations demonstrated the classification model had learned the correct features of each bearing fault. Importantly, there were two testing datasets employed in this study, where the training dataset and testing dataset (2) were completely independent and the number of subspectrograms used for testing was 3-5 times greater than those used for training. Thus, all the results suggest that the proposed method is stable, reliable, suitable for diagnosing different bearing faults and has a great potential in practical applications.

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Section: Introductionmentioning

confidence: 99%

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

Lü

Han

et al. 2020

IEEE Access

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“…DCGAN [17] and bidirectional generative adversarial networks (BiGAN) [18]). This yielded a set of techniques associated with the disciplines of GAN-based works for intelligent fault diagnosis: machinery and electronic systems, including GAN with Adaboost classifier [19], Wasserstein GAN (WGAN) with gradient penalty [20], Auxiliary Classifier GAN [21], GAN network for cross-domain fault diagnosis problems [22], [23], research on the loss of GAN [24] and WGAN with stacked auto-encoders [25]. Nevertheless, considering the fact that multiple fault types and extreme high imbalanced ratios (e.g.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery

Liu

Cheng

2020

IEEE Access

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For the fault diagnosis problems of rotating machinery in the real industrial practice, measurement data with imbalanced class distributions negatively affect the diagnostic performance of most conventional machine learning classification algorithms since equal cost weights are assigned to different fault classes. Meanwhile, the widely used traditional data generation methods for the imbalanced data problem are limited by data dependencies over time continuity. To fill this research gap, this paper develops a new diagnostic framework based on the adversarial neural networks (GAN) and multi-sensor data fusion technique to generate new synthetic data for data compensation purpose. Two different practice modes are designed based on this framework according to the position logic of the data fusion, namely a Pre-fusion GAN mode and a Post-fusion GAN mode. More concretely, without data pre-processing, the designed generator generates synthetic data to puzzle the discriminator and the synthetic data that out-trick the discriminator can be used to compensate the minor class. To avoid data dependency and to ensure the generality of the proposed framework, the network modelling are trained with a more practical approach where the training and test data are obtained under different rotating speeds. Two imbalanced data sets on the rotating machinery, one benchmark public rolling bearing data set and another gear box data set acquired in our lab, are used to validate the proposed method. The performance is examined through a wide range of data imbalanced ratios (as high as 30:1), and compared with other state-of-the-art methods. The experiment results conclude that the proposed Pre-fusion GAN and Post-fusion GAN frameworks both have good performance on the imbalanced fault diagnosis of rotating machinery. INDEX TERMS Generative adversarial networks, imbalanced fault diagnosis, data continuity, rotating machinery.

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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.…”

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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Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty

Cited by 200 publications

References 8 publications

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery

Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks

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