Domain Adaptation based Fault Diagnosis under Variable Operating Conditions of a Rock Drill
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https://orcid.org/0000-0003-3712-3314
Taehun Kim
Jin Uk Ko
Jinwook Lee
Keon Kim
Abstract
Fault diagnosis using a data-driven approach is an essential technology for the safety and maintenance of a rock drill. However, since the signals acquired from the rock drill have different distributions due to variable operating conditions, the classification performance of the data-driven method decreases; this is called the domain shift issue. This paper proposes a new domain adaptation-based fault diagnosis scheme to solve the problem. The proposed method introduces a data cropping technique to mitigate the difference in the length of the data measured from the rock drill for each impact cycle. To extract invariant features for all operating conditions, the proposed method combines two methods: the domain adversarial neural network and minimization of the maximum mean discrepancy (MMD) between the features from different domains. In addition, a soft voting ensemble is used to reduce the model uncertainty. The proposed method shows superior performance when validated with the rock drill dataset and ranked 2nd place in the 2022 PHM Conference Data Challenge.
How to Cite
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Rock Drill, Fault Diagnosis, Domain Adaptation, Deep-learning
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