J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2023 , Vol. 28 >Issue 5: 638 - 651

DOI: https://doi.org/10.1007/s12204-021-2320-6

Machinery and Instrument

Fault Diagnosis for Rolling Element Bearing in Dataset Bias Scenario

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  • (Marine Engineering College, Dalian Maritime University, Dalian 116026, Liaoning, China)

Accepted date: 2021-01-08

  Online published: 2023-10-20

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Abstract

Recently, data-driven methods, especially deep learning, outperform other methods for rolling element bearing (REB) fault diagnosis. Nevertheless, most research work assumes that REB dataset is unbiased. In the real industry applications, the dataset bias exists with REB owing to varying REB working conditions and noise interference. Recently proposed adversarial discriminative domain adaptation (ADDA) is an increasingly popular incarnation to solve dataset bias problem. However, it mainly devotes to realizing domain alignments, and ignores class-level alignments; it can cause degradation of classification performance. In this study, we propose a new REB fault diagnosis model based on improved ADDA to address dataset bias. The proposed diagnosis model realizes domain- and class-level alignments in dataset bias scenario; it consists of two feature extractors, a domain discriminator, and two label classifiers. The feature extractors and domain discriminator are trained in an adversarial manner to minimize the domain difference in feature extractors. The domain discrepancy in label classifier is reduced by minimizing correlation alignment (CORAL) loss. We evaluate the proposed model on the Case Western Reserve University (CWRU) bearing dataset and Paderborn University bearing dataset. The proposed method yields better results than other methods and has good prospects for industrial applications.

Key words： rolling element bearing (REB), dataset bias, adversarial discriminative domain adaptation (ADDA), correlation alignment (CORAL) loss

Cite this article

HOU Liangsheng(侯良生),ZHANG Jundong*(张均东) . Fault Diagnosis for Rolling Element Bearing in Dataset Bias Scenario[J]. Journal of Shanghai Jiaotong University(Science), 2023 , 28(5) : 638 -651 . DOI: 10.1007/s12204-021-2320-6

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