Application of motor fault detection based on symbolic time series analysis

Abstract

An improved method of motor detection based on symbolic time series analysis is proposed, and the method adaptively partition off the region which has the most symbols in the symbolic series into two new regions, which enhances the sensitive degree of symbols to the signal. Except that the fuzzy relative entropy is introduced in the paper to improve the reliability of the diagnosis results. Laboratory experiments of fault diagnosis of inductive motor show that comparing with the uniform partition, the new method is more sensitive to the system and also owns a stronger robustness and a better reliability.