Fault Detection of Induction Motor Based on ALO Optimized TKSVDD

Failure of asynchronous motor will cause motor short circuit accident, personal electric shock and other hazards, so it is very important to detect its abnormalities during its operation. In order to solve the problems of low detection accuracy and inaccurate detection results in asynchronous motor detection, a fault detection method of asynchronous motor based on ant lion optimizer optimizes three kernel support vector data description (ALO-TKSVDD) is proposed in this paper. Firstly, for the current signal of asynchronous motor, stochastic resonance is used to improve the signal-to-noise ratio; Secondly, ant lion optimizer (ALO) is used to optimize the three kernel support vector data description (TKSVDD) to detect abnormal data of the target signal; Finally, the accuracy and feasibility of ALO-TKSVDD are verified. Comparative experiments show that the asynchronous motor anomaly detection method proposed in this paper has the highest accuracy and the lowest false detection rate.