Journal ArticleDOI
Deep Learning Fault Diagnosis Method Based on Global Optimization GAN for Unbalanced Data
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TLDR
New generator and discriminator of Generative Adversarial Network (GAN) are designed in this paper to generate more discriminant fault samples using a scheme of global optimization to solve the problem of unbalanced fault samples.Abstract:
Deep learning can be applied to the field of fault diagnosis for its powerful feature representation capabilities. When a certain class fault samples available are very limited, it is inevitably to be unbalanced. The fault feature extracted from unbalanced data via deep learning is inaccurate, which can lead to high misclassification rate. To solve this problem, new generator and discriminator of Generative Adversarial Network (GAN) are designed in this paper to generate more discriminant fault samples using a scheme of global optimization. The generator is designed to generate those fault feature extracted from a few fault samples via Auto Encoder (AE) instead of fault data sample. The training of the generator is guided by fault feature and fault diagnosis error instead of the statistical coincidence of traditional GAN. The discriminator is designed to filter the unqualified generated samples in the sense that qualified samples are helpful for more accurate fault diagnosis. The experimental results of rolling bearings verify the effectiveness of the proposed algorithm.read more
Citations
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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane,Jongwon Seok +1 more
TL;DR: This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms and can be of good help for future researchers to start their work on machinery fault Detection and diagnosis using the C WRU dataset.
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Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
TL;DR: A normalized convolutional neural network is proposed for the diagnosis of different fault severities and orientations considering data imbalance and variable working conditions and results show that the proposed method has excellent diagnosis accuracy and admirable robustness, and also has sufficient stability on the data imbalance.
References
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Proceedings ArticleDOI
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