Advancing Predictive Maintenance: A Study of Domain Adaptation for Fault Identification in Gearbox Components

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Published Sep 4, 2023
Shinya Tsuruta Koji Wakimoto Takaaki Nakamura Shahin Siahpour Marcella Miller John Taco Jay Lee

Abstract

This paper explores the use of machine learning in predictive maintenance, which has been increasingly demanded in recent years to reduce downtime and maintenance burden. The challenge of different data distributions between training and test data in machine learning is common in predictive maintenance where equipment operation patterns can change, leading to reduced operational efficiency. The authors validate a domain-adaptive anomaly detection method combining CNN and MMD, which achieves similar accuracy with PCA, SVD, and other dimensionality reduction methods. The study also shows that the method maintains accuracy even when the number of normal data in the target domain is 1/10 of the source domain.  

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Keywords

predictive maintenance, domain adaptation, machine learning, convolutionnal neural network, maximum mean discrepancy

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Section
Special Session Papers