In order to solve the problems of high cost, large volume and difficult maintenance when traditional mechanical sensors are used in permanent magnet synchronous motor, a sensorless system model of permanent magnet synchronous motor based on untracked Kalman observer is established in this paper, and the running state of permanent magnet synchronous motor is predicted by untracked Kalman algorithm to achieve closed-loop control. The simulation results show that the traceless Kalman observer has the advantages of accurate prediction, good dynamic and static performance, and strong anti-interference ability. It solves the defects of traditional permanent mechanical sensors to a large extent and has great application prospects in the future.
In order to achieve accurate diagnosis of motor faults, a technique based on wavelet analysis and RBF neural networks is used. The wavelet thresholding method is first used to reduce the noise of the motor sound and improve the signal-to-noise ratio in order to further extract fault features. Then the wavelet packet method is used to analyze the sound signals of the three-phase asynchronous motor in three states to extract the band energy, and finally the band energy is fed into the neural network for training to build a classifier for fault diagnosis. The experimental results show that the method of combining wavelet packet technology and RBF neural network has less time consumption and higher accuracy in diagnosing motor faults. It has the potential for further development.
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