For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Many redundant features will lead to a decrease in diagnosis efficiency and increase the computation cost, so it is necessary to eliminate redundant features and features that have negative effects. This paper presents a novel supervised filter feature selection method for reducing data dimension by employing the Gaussian probability density function (GPDF) and named Gaussian vote feature selection (GVFS). To evaluate the effectiveness of the proposed GVFS, we compared it with the other five filter feature selection methods by utilizing the PMDCM’s data. Additionally, Gaussian naive Bayes (GNB), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) are utilized for the construction of fault diagnosis models. Experimental results show that the proposed GVFS has a better diagnostic effect than the other five feature selection methods, and the average accuracy of fault diagnosis improves from 97.89% to 99.44%. This paper lays the foundation of fault diagnosis for PMDCMs and provides a novel filter feature selection method.

For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. Only using the signal features of current in a single segment is not conducive to fault diagnosis for PMDCMs. In this work, multi-segment feature extraction is presented for improving the effect of fault diagnosis of PMDCMs. Additionally, a support vector machine (SVM), a classification and regression tree (CART), and the k-nearest neighbor algorithm (k-NN) are utilized for the construction of fault diagnosis models. The time domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Experimental results show that multi-segment features have a better diagnostic effect than single-segment features; the average accuracy of fault diagnosis improves by 19.88%. This paper lays the foundation of fault diagnosis for PMDCMs through multi-segment feature extraction and provides a novel method for feature extraction.