Mechanical Fault Diagnosis of a Disconnector Operating Mechanism Based on Vibration and the Motor Current

Zhenming Zhang, Chenlei Liu, Rui Wang, Jian Li, Di Xiahou, Qinzhe Liu, Shi Cao and Shengrui Zhou
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Zhenming Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China
Chenlei Liu: School of Electrical Engineering, Shandong University, Jinan 250061, China
Rui Wang: Shandong Taikai Disconnector Co., Ltd., Tai’an 271000, China
Jian Li: Shandong Taikai Disconnector Co., Ltd., Tai’an 271000, China
Di Xiahou: School of Electrical Engineering, Shandong University, Jinan 250061, China
Qinzhe Liu: School of Electrical Engineering, Shandong University, Jinan 250061, China
Shi Cao: School of Electrical Engineering, Shandong University, Jinan 250061, China
Shengrui Zhou: School of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2022, vol. 15, issue 14, 1-17

Abstract: The mechanical fault diagnosis of a disconnector operating mechanism using a single signal is not sufficiently accurate and reliable. To address this problem, this paper proposes a new fault diagnosis method based on the vibration signal and the motor current signal. First, based on the analysis of the motor stator current signal envelope, segmented envelope RMS values are extracted. Then, the vibration signal of the operating mechanism is processed with VMD (Variational Mode Decomposition). In this paper, the number of modal decompositions K is selected according to the envelope entropy. Second, the effective value of the current segment envelope is fused with the energy entropy value of each IMF component to construct the feature parameters for fault identification. Finally, a fusion weighting algorithm using AdaBoost is proposed to train an SVM as a strong classifier to improve the correct fault diagnosis rate. In this paper, the proposed new diagnosis method is applied to a 220 kV disconnector operating mechanism. The algorithm can effectively identify three operating states of a disconnector operating mechanism.

Keywords: fusion diagnosis; disconnector operating mechanism; vibration signal; motor current signal; AdaBoost?SVM (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: 2022
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