Abstract
This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.
Highlights
In the industrial era, the application of motors has been very extensive and versatile and they have become indispensable equipment on the production line
After the current signal is characterized and normalized, a back propagation (BP) neural network, particle swarm optimization (PSO)-BP neural network, and improved particle swarm optimization–back propagation (PSO-BP) neural network are used as the classifier to identify healthy motors, bearing damage motors, stator winding inter-turn short circuit motors, and broken rotor bar motors
This paper analyzed the current signal of a healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor
Summary
The application of motors has been very extensive and versatile and they have become indispensable equipment on the production line. This paper used motor current signature analysis (MCSA) [2] on the current signals from healthy motors and three types of motor faults, including: (1) bearing damage motor, (2) stator winding inter-turn short circuit motor, and (3) broken rotor bar motor. MCSA is used to observe the change of time or frequency of the motor failure current signal. After the current signal is characterized and normalized, a BP neural network, PSO-BP neural network, and improved PSO-BP neural network are used as the classifier to identify healthy motors, bearing damage motors, stator winding inter-turn short circuit motors, and broken rotor bar motors. Common methods include fast Fourier transform by frequency and energy, and wavelet transform by frequency, energy, and time, as a reference for maintenance engineers to determine the types of failure. WT can be divided into continuous wavelet transform (CWT) and discrete wavelet transform (DWT)
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