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A Semi-Supervised Fault Diagnosis Method Based on Improved Bidirectional Generative Adversarial Network
| Content Provider | MDPI |
|---|---|
| Author | Cui, Long Tian, Xincheng Shi, Xiaorui Wang, Xiujing Cui, Yigang |
| Copyright Year | 2021 |
| Description | With the assumption of sufficient labeled data, deep learning based machinery fault diagnosis methods show effectiveness. However, in real-industrial scenarios, it is costly to label the data, and unlabeled data is underutilized. Therefore, this paper proposes a semi-supervised fault diagnosis method called Bidirectional Wasserstein Generative Adversarial Network with Gradient Penalty (BiWGAN-GP). First, by unsupervised pre-training, the proposed method takes full advantage of a large amount of unlabeled data and can extract features from vibration signals effectively. Then, using only a few labeled data to conduct supervised fine-tuning, the model can perform an accurate fault diagnosis. Additionally, Wasserstein distance is used to improve the stability of the model’s training procedure. Validation is performed on the bearing and gearbox fault datasets with limited labeled data. The results show that the proposed method can achieve 99.42% and 91.97% of diagnosis accuracy on the bearing and gear dataset, respectively, when the size of the training set is only 10% of the testing set. |
| Starting Page | 9401 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11209401 |
| Journal | Applied Sciences |
| Issue Number | 20 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-10 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Generative Adversarial Networks Semi-supervised Learning Fault Diagnosis Wasserstein Distance |
| Content Type | Text |
| Resource Type | Article |
