Research on the Detection Method of Excessive Spark in Ship DC Motors Based on Wavelet Analysis

Chaoli Jiang, Lubin Chang (), Guoli Feng, Yuanshuai Liu and Wenli Fei
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Chaoli Jiang: College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
Lubin Chang: College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
Guoli Feng: College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
Yuanshuai Liu: College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
Wenli Fei: College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China

Energies, 2025, vol. 18, issue 17, 1-18

Abstract: In order to analyze and solve the problem of excessive commutation spark of DC motor in ship electric propulsion system, which leads to a decrease in output power and low torque, this paper first establishes a mathematical model of the ship DC motor, builds its simulation model based on the mathematical model, and conducts simulation verification. Secondly, the Cassie arc model is introduced to model the commutation spark, and the Cassie arc model is connected in series in the armature winding of the DC motor to achieve virtual injection of excessive spark fault of the DC motor. Finally, the Fourier transform and wavelet analysis are used to process the data of the armature winding current and excitation current of the DC motor. The simulation results show that when an arc fault occurs in the DC motor, the ripple coefficient of the armature current and excitation current will increase, and the high-frequency component will increase. DB8 is an adopted wavelet function that decomposes the armature current and excitation current six times, and calculates the energy changes before and after the fault of each decomposed signal layer. It is found that without considering the approximate components, the D4 layer wavelet energy of the armature current and excitation current has the largest proportion in the detail components. The D1, D2, and D3 layers’ wavelet decomposition signals of the armature current and excitation current have significant energy changes; that is, the energy increase in the middle and high frequency parts exceeds 20%, and the D3 layer wavelet decomposition signal has the largest energy change, exceeding 40%. This can be used as a fault characteristic quantity to determine whether the DC motor has a large spark fault. This study can provide reference and guidance for online detection technology of excessive sparks in ship DC motors.

Keywords: excessive spark; Cassie; Fourier transform; wavelet analysis; fault characteristic quantity (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: 2025
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