Domain Adaptation based Fault Diagnosis under Variable Operating Conditions of a Rock Drill

Abstract

Data-driven fault diagnosis is an essential technology for the safety and maintenance of rock drills. However, since the signals acquired from a rock drill have different distributions, which arise due to their variable operating conditions, the classification performance of any data-driven method is diminished; this is called the domain-shift issue. This paper proposes a new domain-adaptation-based fault diagnosis scheme to solve the domain-shift problem. The proposed method introduces a data-cropping technique to mitigate the difference in the length of the data measured from a rock drill for each impact cycle. To extract invariant features for all operating conditions, the proposed method combines two methods: a 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 a rock drill dataset; the proposed approach was ranked in 2^nd place in the 2022 PHM Conference Data Challenge.