Abstract
With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.
Highlights
In order to verify the performance of the modified gray wolf optimization algorithm (MGWO), two groups of test functions were used to verify the performance of the algorithm
In order to prove the effectiveness of the model proposed in this study for motor fault diagnosis, the experimental results were compared with MGWO-Support vector machine (SVM), sailfish optimization (SFO)-SVM, SVM, ELM and Back Propagation neural network (BP) classification models
It is of great significance to study its fault diagnosis and reliability to maintain the reliability of great significance to study its fault diagnosis and reliability to maintain the reliability of its working system
Summary
The fault diagnosis method based on machine learning means that different types of fault diagnosis can be realized by classifying and learning the features or eigenvectors of different vibration or current signals with the goal of maximizing the classification accuracy of different states [12]. The machine learning method was selected for fault diagnosis in this study, so as to achieve high-precision for fault diagnosis, the foundation for the study of motor maintenance safety and reliability was laid. The structure of the paper is as follows: the Section 2 introduces the relevant theoretical methods and literature review; Section 3 introduces the traditional GWO algorithm and the proposed MGWO, and discusses performance of MGWO; In Section 4, the SVM classification model was introduced and optimized by MGWO. The study work, conclusion and future work of this study were summarized
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