JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (5): 63-75.doi: 10.6040/j.issn.1671-9352.0.2022.130

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Signal detection and fault diagnosis based on improved particle swarm optimization algorithm

ZHANG Jinke, ZHANG Jiangang*   

  1. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
  • Published:2023-05-15

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Abstract: A piecewise bistable stochastic resonance system is investigated, and the parameters of the bistable system are optimized by using the improved particle swarm optimization(PSO)algorithm, which is applied to weak signal detection and bearing fault diagnosis. First, the piecewise potential function is introduced, and the output signal-to-noise ratio of the system is derived theoretically. Next, the effect of the average first passage time and signal-to-noise ratio on the system parameters from the transition of particles in the potential wells are discussed and analyzed, and the model is evaluated. Second, by combining the random weight particle swarm optimization algorithm and adaptive weight particle swarm optimization algorithm with stochastic resonance, and taking the signal-to-noise ratio of the output signal as the evaluation index, the system parameters are optimized and adjusted. The improved algorithms of the two particle swarm optimization algorithms are compared. Finally, the improved particle swarm optimization algorithm is applied to fault diagnosis. The effectiveness and superiority of the random weight particle swarm optimization algorithm are verified by comparing the output effects of several algorithms.

Key words: piecewise potential function, stochastic weighted particle swarm optimization algorithm, weak signal detection, fault diagnosis

CLC Number: 

  • O193
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