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A New Method for Rubbing Fault Identification Based on the Combination of Improved Particle Swarm Optimization with Self-Adaptive Stochastic Resonance

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Abstract

To fulfill the effective diagnosis of the rubbing fault between the rotor and the stator, the combination strategy of adaptive weight particle swarm optimization (PSO) and general scale transformation stochastic resonance (GSTSR) is proposed in the paper. Firstly, in view of the self-adaptive weighted PSO featured by high precision and quick convergence, the method has made self-adaptive adjustment of systematic parameters based on PSO algorithm (with signal–noise ratio as fitness function). Secondly, GSTSR can further highlight the characteristic information otherwise covered by noise. Therefore, self-adaptive weighted PSO algorithm is combined with GSTSR to make characteristic extraction of rotor–stator rubbing faults. Finally, a comparative analysis with other methods and the analysis of faults in different states all indicate that the combination of self-adaptive weighted PSO algorithm and GSTSR can enhance rubbing fault characteristics and has effective identification of rotor–stator rubbing faults.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China [Grant number: 51605309], Natural Science Foundation of Liaoning Province [Grant number: 2019-ZD-0219] and Aeronautical Science Foundation of China [Grant number: 201933054002].

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  1. Shenyang Aerospace University, Shenyang, China

    Haonan Cong, Mingyue Yu, Yunhong Gao & Minghe Fang

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  1. Haonan Cong
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  2. Mingyue Yu
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  3. Yunhong Gao
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  4. Minghe Fang
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Correspondence to Mingyue Yu.

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Cong, H., Yu, M., Gao, Y. et al. A New Method for Rubbing Fault Identification Based on the Combination of Improved Particle Swarm Optimization with Self-Adaptive Stochastic Resonance. J Fail. Anal. and Preven. 22, 690–703 (2022). https://doi.org/10.1007/s11668-022-01365-1

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