Fuzzy clustering for feature extraction in wavelet-based fault gear identification of electrical machines

Article type: Research Article

Authors: Wang, Yao^{; *} | Yu, Tao | Luo, Tianmin | Ye, Haojie | Pan, Yiru

Affiliations: CHN ENERNGY GROUP Xinjiang Energy Co., LTD., Urumqi, China

Correspondence: [*] Corresponding author. Yao Wang, China Energy Investment Xinjiang Energy Co., Ltd, Urumqi 830000, Xinjiang, China. E-mail: wangyao_vip@outlook.com.

Abstract: Fault detection and diagnosis in electrical machines are periodical for preventing operational interruptions and unexpected shutdowns. However, a Wavelet Feature-dependent Clustering Technique (WFCT) is introduced to address the cyclic fault detection between successive operation intervals. This technique identifies override features from the time-frequency operational wavelets throughout the machine running time. This grouping binds time and operational frequency for identifying override exceeding shutdown/ failure instances. Based on their revamping time, the identified instances are further grouped to prevent overrides in successive operational hours. The fuzzy clustering prevents variation features based on conventional to high-fuzzified extractions.

Keywords: Electrical machines, fault diagnosis, feature extraction, fuzzy clustering, time-frequency wavelet

DOI: 10.3233/JIFS-234256

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2415-2431, 2024