Intelligent fault diagnosis of high-voltage circuit breakers using triangular global alignment kernel extreme learning machine. (March 2021)

• Record Type: Journal Article
• Authors: Chen, Lei; Wan, Shuting
• Is Part Of: ISA transactions. Volume 109(2021)
• Page Start: 368
• Page End: 379
• Publication Date: 2021-03
• Subjects: Intelligent fault diagnosis -- High-voltage circuit breakers -- Machine learning -- Vibration signals -- Sampling asynchrony -- Kernel extreme learning machine; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.10.018
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In recent years, vibration-based intelligent fault diagnosis of high-voltage circuit breakers (HVCBs) exhibits excellent performance. It requires a reliable machine learning method to develop an automatically diagnostic model to recognize the mechanical state from vibration signals. However, the traditional machine learning methods tend to produce unstable diagnostic results under the case of sampling asynchrony caused by the fluctuation of the control voltage. To address this problem, an improved kernel extreme learning machine (K-ELM) called triangular global alignment kernel (TGAK) extreme learning machine (TGAK-ELM) was presented in this study, which was developed by introducing TGAK into K-ELM. The TGAK is an elastic kernel which was designed by considering all the possible alignments between samples. Therefore, it provides a flexible similarity measure for samples, resulting in the improvement of the diagnostic performance. Experiments on the 35kV HVCB verified the effectiveness of the proposed method. Compared to other state-of-the-art machine learning methods, the proposed TGAK-ELM produced better diagnostic results. And further experiments on eight datasets picked from UCR repository suggested the applicability of TGAK-ELM in other fields. Highlights: An improved K-ELM was proposed for HVCB fault diagnosis. It uses an elastic kernel mapping samples into a higher dimensional space. It improves the diagnostic accuracy under the case of sampling asynchrony. It is promising in application of other industrial problems.