2023 Domain Adaptation based Fault Diagnosis under Variable Operating Conditions of a Rock Drill
본문
- Journal
- International Journal of Prognostics and Health Management
- Date
- 2023-07
- Citation Index
- IF: 0.0, Rank: 0.0%
- Vol./ Page
- Vol. 14, No. 2, pp. 1-9
- Year
- 2023
- Link
- http://doi.org/10.36001/ijphm.2023.v14i2.3425 171회 연결
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 2nd place in the 2022 PHM Conference Data Challenge.