Key result
PSO-FrSlEn classified 10 kinds of bearing signals with 100% classification accuracy using double features, which was at least 4% higher than four other fractional entropies.
Why the study?
Slope entropy required improvements in distinguishing different signal types and resolving the selection of two threshold parameters.
PSO-FrSlEn provides a highly accurate method for bearing fault diagnosis, outperforming other fractional entropy methods.
Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which has shown excellent performance in the fields of medical and hydroacoustics. In order to improve the ability of SlEn to distinguish different types of signals and solve the problem of two threshold parameters selection, a new time series complexity indicator on the basis of SlEn is proposed by introducing fractional calculus and combining particle swarm optimization (PSO), named PSO fractional SlEn (PSO-FrSlEn). Then we apply PSO-FrSlEn to the field of fault diagnosis and propose a single feature extraction method and a double feature extraction method for rolling bearing fault based on PSO-FrSlEn. The experimental results illustrated that only PSO-FrSlEn can classify 10 kinds of bearing signals with 100% classification accuracy by using double features, which is at least 4% higher than the classification accuracies of the other four fractional entropies.
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Li et al. (2022) studied Bearing Fault Diagnosis. PSO fractional SlEn (PSO-FrSlEn) vs. Other fractional entropies was evaluated on Classification accuracy of 10 kinds of bearing signals. PSO-FrSlEn classified 10 kinds of bearing signals with 100% classification accuracy using double features, which was at least 4% higher than four other fractional entropies.
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