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| Turn-to-Turn Short Circuit Fault Diagnosis of Brushless DC Motor Based on Multiple Magnetic Flux Leakage Signal Arrays |
| Wu Zhenyu1, Wang Hui2, Hu Cungang1, Xi Haotian1, Cao Wenping1 |
1. School of Electrical Engineering and Automation Anhui University Hefei 230601 China;
2. China Electric Power Research Institute Beijing 100048 China |
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Abstract The traditional fault detection method cannot detect or locate inter-turn short-circuit faults. Therefore, this paper develops a new detection method by combining magnetic flux leakage and backpropagation neural networks.
The variation of the leakage magnetic field originates from the stator current and radiates to the outside of the motor. Therefore, it provides information about the state of the stator winding. If a turn-to-turn short circuit fault occurs in the motor, the amplitude of the leakage flux (Å st) emanating from outside the motor changes and can be detected by the flow sensor. A backpropagation neural network is an error correction algorithm that uses multi-layer feedback based on the errors between input and output signals. Hall sensors collect magnetic flux leakage signals, backpropagation neural networks analyze and extract features from the data, and the fault location and situation can be obtained.
There are four types of motor states: health, A-phase fault, B-phase fault, and C-phase fault, and the degree of fault is also divided into four types: μ =1/9, 2/9, 3/9, and 4/9. In the simulation, the inter-turn short circuit is modeled as a turning slot in the faulty phase. Regarding μ =1/9 and 2/9 fault modeling, the Fourier transform is applied to three-phase current, and current sensors obtain amplitude change maps of three-phase current in the time and frequency domains. Then, the Hall sensor obtains the time-domain and frequency-domain waveforms of the magnetic flux leakage signals at three positions. It is found that the frequency spectrum of the magnetic flux leakage signal contains the motor state information and can be used as input for the backpropagation neural network.
Magnetic flux leakage and three-phase current signals are collected from three different positions under four different testing conditions, with the health and fault states of phase A (2/9) for analysis. Fourier analysis is performed on magnetic flux leakage signals, which are used as inputs for backpropagation neural networks to evaluate the degree and location of faults. The experiment shows that (1) the amplitude of leakage flux and current signal regularly increases with the increase of motor speed; (2) When a turn-to-turn short circuit fault occurs, the amplitude of leakage flux and current decreases with the increase of load.
The training accuracy of the fault phase dataset, the four fault degree datasets (1/9-4/9), and the three different fault positions is 99.8%, 98%, and 80%, respectively. The proposed method only collects the external MLF signals of the motor and then performs Fourier transform and BPNN for spectrum analysis and fault diagnosis. Therefore, it is non-intrusive and easy to implement in practice. Potentially, it has a great prospect for engineering applications as the costs of sensors and implementation are low, and the computational resources are already in place for motor drives.
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Received: 11 January 2024
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2025, Vol. 40 
