An Adaptive Method for Multifault Diagnosis of Induction Motor Under Sharp Changing Speed and Load Condition

In industrial production, the speed of the motor is constantly changing due to production demands. However, traditional diagnosis methods cannot guarantee their performance under all circumstance, especially the sharp changing speed condition. To tackle this issue, this article proposes an adaptive method that can be used for multifaults diagnosis of motor under variety working condition, especially for the dramatic changing and unstable working states. First, a time-frequency (TF) parameter and resolution adaptive algorithm is proposed for signal preprocessing. Second, the well-processed signal is resampled by TF diagram and peak search method to eliminate the effect of sudden change in speed. A proposed perception matching algorithm based on the symmetrized dot pattern and convolutional neural networks is implemented for improving the accuracy. Finally, an adaptive model with unified diagnose process is developed to improve the accuracy, efficiency, and practicality of the diagnose model. Compared with other state-of-art methods, the results show the out-performance of the proposed method under both in steady and transient state, especially in the case that the speed is changing fast.

Automated summary

This article proposes an adaptive method for multifaults diagnosis of motors under various working conditions, especially in cases of dramatic speed changes and unstable working states. The method includes signal preprocessing using a time-frequency parameter and resolution adaptive algorithm, resampling the well-processed signal, implementing a perception matching algorithm, and developing an adaptive model with a unified diagnose process. The proposed method shows superior performance compared to other state-of-art methods, especially in cases of fast speed changes.