Abstract
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.
Highlights
Rolling bearing is one of the parts of rotating machinery, which are widely used and damaged in mechanical equipment [1,2,3,4]
This paper proposes an optimized adaptive local iterative filtering based on particle swarm optimization
Based on the above theoretical analysis, the adaptive local iterative filtering (ALIF) algorithm based on particle swarm optimization (PSO) is applied to the simulation signal analysis of rolling bearing and fault feature extraction of experimental system, which verifies the effectiveness of the method in fault diagnosis
Summary
Rolling bearing is one of the parts of rotating machinery, which are widely used and damaged in mechanical equipment [1,2,3,4]. The value of the permutation entropy can reflect the complexity of the signal, so as to select the optimal component The advantage of this improved method is that the parameter selection and decomposition results of ALIF algorithm are not affected by human experience. An improved ALIF decomposition method is proposed, which joins the particle swarm optimization (PSO) and permutation entropy (PE) to select the optimal components. To verify the effectiveness of the proposed method, the bearing fault vibration signal measured from test-bed is used for the result evaluation. This paper was organized as follows: the basic principle and characteristics of feature extraction for bearing fault based on Optimized adaptive local iterative filtering and permutation entropy (PE).
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