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

Abhijeet Ainapure, Shahin Siahpour, Xiang Li, Faray Majid and Jay Lee
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Abhijeet Ainapure: Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Shahin Siahpour: Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Xiang Li: Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
Faray Majid: Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Jay Lee: Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA

Mathematics, 2022, vol. 10, issue 3, 1-17

Abstract: 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.

Keywords: fault diagnosis; domain adaptation; noisy label; deep learning; convolutional neural network; rotating machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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