Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

Han, Lei; Zhou, Jianzhong; Zheng, Yuan; Peng, Xuanlin; Jiang, Wei

doi:10.1016/j.neucom.2018.07.034

“…A GAN offers a new methodology to draw realistic samples from an unknown distribution with the promise of utilizing large volumes of unlabeled training data for unsupervised learning making them one of the hottest research areas in machine learning/artificial intelligence. Since the introduction by Goodfellow in 2014, GAN has received great attention and have been used in various applications [27][28][29][30][31]. However, the original GAN has problems such as training instability, lack of diversity in generating samples, and the loss of generator and discriminator cannot indicate the training process [29], [32][33][34].…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Day-ahead renewable scenario forecasts based on generative adversarial networks

Jiang¹,

Mao²,

Chai³

et al. 2021

Preprint

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<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>

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“…Generative adversarial networks offer a new framework for drawing realistic samples from an unknown data distribution, and they are therefore currently one of the most active research areas in artificial intelligence. Since their introduction by Goodfellow in 2014, GANs have become a popular approach for a variety of applications [25][26][27]. However, GANs can be remarkably difficult to train.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Day-ahead renewable scenario forecasts based on generative adversarial networks

Jiang¹,

Mao²,

Chai³

et al. 2020

Preprint

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<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>

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“…It used training and test sets of feature vectors. In the literatures [29][30][31][32], neural networks were used for fault diagnosis [29,30], controlling a temperature eld [31], prediction of speech quality [32], and classi cation of emotion recognition [33]. e authors used three-layer backpropagation neural network for data classi cation (input layer, hidden layer, and output layer).…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

Vibration‐Based Fault Diagnosis of Commutator Motor

Głowacz

¹

,

Głowacz

²

2018

Shock and Vibration

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This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

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Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

Cited by 201 publications

References 19 publications

Day-ahead renewable scenario forecasts based on generative adversarial networks

Day-ahead renewable scenario forecasts based on generative adversarial networks

Day-ahead renewable scenario forecasts based on generative adversarial networks

Vibration‐Based Fault Diagnosis of Commutator Motor

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