Journal article
Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings
Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science, Vol.231(4), pp.635-654
02/01/2017
DOI: 10.1177/0954406215623311
Abstract
The bearing fault diagnosis is of vital significance in maintaining the safety of rotation machine. Among various fault detection techniques, the diagnosis based on vibration signal is widely applied in monitoring the condition of rotation machine. Variational mode decomposition (VMD) is a novel signal analysis method, which can decompose a multi-component signal into a certain number of band-limited intrinsic mode functions (BLIMFs) nonrecursively. VMD could overcome some problems such as mode mixing, the inference of noise, the determination of wavelet base, which exist in empirical mode decomposition, ensemble empirical mode decomposition, wavelet transform, respectively. However, the empirical selection of the parameters for VMD would affect the result of the decomposition. This paper presents an adaptive VMD method with parameter optimization for detecting the localized faults of rolling bearing. Kurtosis, sensitive to transient impulsive components, is employed as optimization index to evaluate the performance of the VMD. Two parameters in the VMD, namely the number of decomposition modes and data-fidelity constraint, are optimized synchronously based on the kurtosis index through artificial fish swarm algorithm. Executing VMD with the acquired parameters, the optimal BLIMF is obtained. The spectrum analysis of the optimal BLIMF could identify the characteristic frequency caused by the localized crack effectually. The validity of the proposed method is proved by means of a cyclic transient impulse response signal and two experiments with practical vibration signals of rolling bearings. Compared to several existing methods, the proposed method demonstrates reinforced results.
Details
- Title: Subtitle
- Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings
- Creators
- Jun Zhu - Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, ChinaChao Wang (Author)Zhiyong Hu - Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, ChinaF Kong - Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, ChinaXingchen Liu - Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Resource Type
- Journal article
- Publication Details
- Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science, Vol.231(4), pp.635-654
- Publisher
- SAGE Publications
- DOI
- 10.1177/0954406215623311
- ISSN
- 0954-4062
- eISSN
- 2041-2983
- Language
- English
- Date published
- 02/01/2017
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984222055702771
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