Non-stationary Fault Feature Extraction for Gear in Electric Powertrain of Electric Vehicle

doi:10.3901/JME.2025.20.223

Abstract

Abstract: Due to the inability to obtain speed signals in the vehicle environment, it is difficult to extract gear fault features of the electric powertrain under non-stationary conditions. To address this issue, a gear vibration feature extraction method based on synchronous compression transformation and sparse decomposition in the angular frequency domain under non-stationary conditions is proposed. This method takes advantage of the integrated structure of electric powertrain. Firstly, synchronous compression transformation is performed on the vibration acceleration signal to improve the time-frequency aggregation of the switching frequency harmonics and obtain accurate time-frequency ridge. Secondly, based on the switching frequency harmonic time-frequency ridge, the motor speed is calculated to obtain the speed of each axis of the transmission. Based on the speed signal, angular domain sampling is conducted. Finally, a sparse decomposition model based on the generalized minimax concave penalty function is established. The gear meshing order and its modulation order are reconstructed in the angular frequency domain. The accuracy of the proposed method is verified through simulation, experimental platform, and vehicle. The results show that the proposed method can extract non-stationary fault features of gears using only vibration acceleration sensors, and improve the efficiency of gear fault diagnosis in electric powertrain.

Key words: electric powertrain, non-stationary condition, switching frequency, gear fault

CLC Number:

U469
TH165

LIU Kun, WU Wei, CHEN Xing, YUAN Shihua. Non-stationary Fault Feature Extraction for Gear in Electric Powertrain of Electric Vehicle[J]. Journal of Mechanical Engineering, 2025, 61(20): 223-233.

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