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Open AccessArticle

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis

by Yong Lv 1,2, Yi Zhang 1,2,* and Cancan Yi 1,2
1
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China
2
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(12), 920; https://doi.org/10.3390/e20120920
Received: 16 October 2018 / Revised: 26 November 2018 / Accepted: 27 November 2018 / Published: 1 December 2018
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed. View Full-Text
Keywords: adaptive local iterative filtering; particle swarm optimization; permutation entropy; fault diagnosis adaptive local iterative filtering; particle swarm optimization; permutation entropy; fault diagnosis
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Lv, Y.; Zhang, Y.; Yi, C. Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis. Entropy 2018, 20, 920.

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