Benchmarking domain adaptation techniques for bearing fault diagnosis

• Victor Afonso Bauler UFSC
• Júlio A. Cordioli UFSC
• Danilo Braga Dynamox SA
• Danilo Silva UFSC

DOI: https://doi.org/10.5753/eniac.2024.244961

Abstract

Technological advancements have enabled the simultaneous monitoring of thousands of machines, making it possible to train machine learning algorithms for automated fault detection and diagnosis. However, one of the main challenges has been the lack of labeled data, as well as the fact that the same type of machine can be exposed to different operating conditions, altering the distribution of the acquired signal attributes. A potential solution is to use domain adaptation techniques, which can improve model performance on new types of machines, even without labeled data. However, many of the studies presented in the literature face data leakage issues, which make it impossible to compare techniques since the models performance is unrealistic. Therefore, this paper proposes a methodology for comparing domain adaptation techniques that avoids this problem. Eight domain adaptation techniques were implemented and evaluated on two datasets, resulting in significant improvements in one of the evaluation scenarios.

Keywords: Artificial Neural Networks, Deep Learning, Machine Learning, Domain Adaptation

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