A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

Cheng Peng, Lingling Li, Qing Chen, Zhaohui Tang, Weihua Gui and Jing He
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Cheng Peng: School of Computer, Hunan University of Technology, Zhuzhou 412007, China
Lingling Li: School of Computer, Hunan University of Technology, Zhuzhou 412007, China
Qing Chen: School of Computer, Hunan University of Technology, Zhuzhou 412007, China
Zhaohui Tang: School of Automation, Central South University, Changsha 410083, China
Weihua Gui: School of Automation, Central South University, Changsha 410083, China
Jing He: School of Computer, Hunan University of Technology, Zhuzhou 412007, China

Energies, 2021, vol. 14, issue 4, 1-18

Abstract: Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. Experimental results show that this method has good fault diagnosis performance.

Keywords: fault diagnosis; rolling bearings; unbalance samples; deep transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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