Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels

Mathematics

Jay Lee¹  Abhijeet Ainapure¹  Shahin Siahpour¹  Faray Majid¹  Xiang Li² 

[1] Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA;Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China;

关键词: fault diagnosis;    domain adaptation;    noisy label;    deep learning;    convolutional neural network;    rotating machine;   

DOI  :  10.3390/math10030455

来源: DOAJ

【 摘 要 】

Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasing attention. However, the existing methods in the literature are generally lower compared to environmental noise and data availability, and it is difficult to achieve promising performance under harsh practical conditions. This paper proposes a new cross-domain fault diagnosis method with enhanced robustness. Noisy labels are introduced to significantly increase the generalization ability of the data-driven model. Promising diagnosis performance can be obtained with strong noise interference in testing, as well as in practical cases with low-quality data. Experiments on two rotating machinery datasets are carried out for validation. The results indicate that the proposed algorithm is well suited to be applied in real industrial environments to achieve promising performance with variations of working conditions.

【 授权许可】

Unknown