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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.1
- 수록면
- 87 - 96 (10page)
- DOI
- 10.5370/KIEE.2024.73.1.87
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이 논문의 연구 히스토리 (7)
2024
2023
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초록· 키워드
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In this paper, a practical data acquisition method and fault diagnosis method for applying motor fault diagnosis in the field are studied. Since it is very difficult to acquire fault data in the field, unsupervised learning methods that can be trained using only normal state data are mainly used. However, unsupervised learning methods are very vulnerable to disturbances and are difficult to express the fault level. Disturbances in the field include electrical noise, which can cause data acquisition devices to fail due to low power quality in the field and affect the measured signal, and mechanical noise, such as external vibrations, which directly affect the operation of the electric motor. To minimize the impact of electrical noise, it is common to use hardware filters. Unlike previous studies that use theoretical cutoff frequency setting methods, this paper proposes a method to find the appropriate cutoff frequency using LRP(Layer-wise Relevance Propagation) analysis, one of the XAI(eXplainable A.I) techniques, for practical data acquisition. Fault diagnosis in the field requires robust unsupervised learning algorithms that can ignore the presence of mechanical noise in the signal, such as transient shocks. This is solved by post-processing the output of the auto-encoder with a moving average filter. To represent the fault level a data generation technique, the mix-up algorithm, is used. A method is proposed to threshold the auto-encoder multiple times with data generated by the mix-up algorithm. This proposed method shows the availability of motor fault diagnosis considering the on-site data.
목차
Abstract
1. 서론
2. 이론
3. 데이터 분류 실험
4. 결론
References
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