A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors

Liping Wang¹  Sujuan Shao²  Chicheng Ma²  Zhongwei Zhang²  Mingyu Shao² 

[1] School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China;School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China;

关键词: fault diagnosis;    samples class imbalance;    manifold regularization;    maximum variance discrepancy;    domain adaptation;   

DOI  :  10.3390/s21103382

来源: DOAJ

【 摘 要 】

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

【 授权许可】

Unknown