Permanent Magnet DC Motor Fault Diagnosis Based on Multi-Segment Feature Extraction
For permanent magnet DC motors(PMDCM),the amplitude of the current signal gradually decreases after the motor starts.Using only single segment current signal features is not conducive to fault diagnosis of permanent magnet DC motors.Therefore,a multi-segment feature extraction method is proposed to improve the fault diagnosis effect of permanent magnet direct drive microcontrollers.In addition,support vector machine(SVM),classification and regression tree(CART),and k-nearest neighbor algorithm(k-NN)are used to construct a fault diagnosis model.The time-domain features extracted from multiple continuous current signal segments form a feature vector for fault diagnosis in PMDCM.The results show that compared with single segment features,multi-segment features have better diagnostic performance,and the average accuracy of fault diagnosis has increased by 19.88%.The multi-segment feature extraction lays the foundation for the fault diagnosis of PMDCM and provides a novel feature extraction method.
multi-segment featuresextractionpermanent magnet DC motorfault detection
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