지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.4
- 발행연도
- 2023.8
- 수록면
- 290 - 299 (10page)
- DOI
- 10.5573/IEIESPC.2023.12.4.290
이용수
초록· 키워드
오류제보하기
The study of fault identification of vibration signals from rotating machinery is essential for enhancing industrial production safety. A method combining a capsule network and frequency-slicing wavelet transform is proposed to improve the fault identification accuracy, considering the problem that the original vibration signal of rotating machinery carries multiple noises. The capsule network learning model was also optimized using a dynamic weighting method based on the channel attention mechanism, considering the variable operating conditions of rotating machinery. The dynamic weighting algorithm based on the channel attention mechanism used in the study achieved the highest fault recognition rates, with 99.65%, 99.25%, and 99.90% on sensor 1, sensor 2, and feature fusion data, respectively. Hence, the proposed model for fault identification in rotating machinery vibration signals is superior to other models.
목차
Abstract
1. Introduction
2. Related Work
3. Frequency-slicing Wavelet Transform and Improved Capsule Network based on Rotating Machinery Vibration Fault Signal Identification Model
4. Utility Analysis of a Rotating Machinery Fault Identification Model based on Improved Wavelet Transform and Improved Capsule Network
5. Conclusion
References
참고문헌 (23)
참고문헌 신청
A. D. Makwana, G. S. Patange, “Strategic implementation of 5S and its effect on productivity of plastic machinery manufacturing company”. Australian Journal of Mechanical Engineering, 2022, 20(1). pp. 111-120.
A. Heng, S. Zhang, A. C. C. Tan, “Rotating machinery prognostic: state of the art, challenges and opportunities”. Mechanical systems and signal processing, 2009, 23(3), pp. 724-739.
S. Edwards, A. W. Lees, M. I. Friswell, “Fault diagnosis of rotating machinery”. Shock and vibration digest, 1998, 30(1), pp. 4-13.
Y. Lei, J. Lin, Z. He, “A review on empirical mode decomposition in fault diagnosis of rotating machinery”. Mechanical systems and signal processing, 2013, 35(1-2), pp. 108-126.
W. Qiao, Z. Li, W. Liu, “Fastest-growing source prediction of US electricity production based on a novel hybrid model using wavelet”, International Journal of Energy Research, 2022, 46(2), pp. 1766-1788.
A. Heng, S. Zhang, A. C. C. Tan, “Rotating machinery prognostic: state of the art, challenges and opportunities”. Mechanical systems and signal processing, 2009, 23(3), pp. 724-739.
S. Edwards, A. W. Lees, M. I. Friswell, “Fault diagnosis of rotating machinery”. Shock and vibration digest, 1998, 30(1), pp. 4-13.
Y. Lei, J. Lin, Z. He, “A review on empirical mode decomposition in fault diagnosis of rotating machinery”. Mechanical systems and signal processing, 2013, 35(1-2), pp. 108-126.
W. Qiao, Z. Li, W. Liu, “Fastest-growing source prediction of US electricity production based on a novel hybrid model using wavelet”, International Journal of Energy Research, 2022, 46(2), pp. 1766-1788.
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