Research on Fault Feature Extraction Method for Hydroelectric Generating Unit Based on Improved FMD and CDEI

Tao Wu, Haipeng Gong (), Zaiming Geng, Jian Deng and Fang Yuan ()
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Tao Wu: China Yangtze Power Co., Ltd., Yichang 443000, China
Haipeng Gong: China Yangtze Power Co., Ltd., Yichang 443000, China
Zaiming Geng: China Yangtze Power Co., Ltd., Yichang 443000, China
Jian Deng: China Yangtze Power Co., Ltd., Yichang 443000, China
Fang Yuan: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China

Energies, 2024, vol. 17, issue 23, 1-11

Abstract: As core pieces of equipment in hydropower generation, the operational condition of critical components such as the rotor and thrust bearing is crucial for the stability of hydropower units. The essence of fault diagnosis for hydroelectric generating units is pattern recognition. To achieve high recognition accuracy, it is necessary to maximize the distinguishability of different fault features. However, traditional time–frequency signal processing methods seldom consider this issue during the decomposition process, resulting in low sensitivity of the extracted features to different fault types. To address this issue, this paper proposes a fault feature extraction method for hydroelectric generating units based on Feature Modal Decomposition (FMD) and the Comprehensive Distance Evaluation Index (CDEI). By improving the FMD algorithm, the objective function for selecting modal components during the FMD decomposition process is set as the CDEI, which can measure the sensitivity of fault features, thereby enhancing the distinguishability of the obtained fault features. Next, the Distance Evaluation Index (DEI) is used to measure the sensitivity of the obtained features, and the most sensitive features are selected. Experiments using a rotor test bench and actual signals before and after thrust bearing horizontal adjustment from a hydroelectric generating unit were conducted and compared with related methods. The results show that the proposed method can effectively improve the sensitivity of the obtained fault features and achieve accurate fault diagnosis for hydroelectric generating units.

Keywords: hydroelectric generating unit; vibration signal; feature extraction; feature mode decomposition; thrust bearing horizontal adjustment; distance evaluation index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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