基于改进粒子群优化算法的信号检测及故障诊断

doi:10.6040/j.issn.1671-9352.0.2022.130

摘要/Abstract

摘要： 研究了一种分段双稳态随机共振系统,使用改进的粒子群优化(particle swarm optimization, PSO)算法对双稳系统的参数进行优化,将其应用于弱信号检测以及轴承的故障诊断。首先,引入分段的势函数,对系统的输出信噪比进行理论推导,从势阱中粒子的跃迁角度讨论分析了系统各参数对平均首次通过时间以及信噪比的影响,并借此对系统进行评价;其次,利用随机权重粒子群优化算法和自适应权值粒子群算法,分别与随机共振相结合,以输出信号的信噪比作为评价指标,对系统参数进行优化调节,并比较2种粒子群优化算法的改进算法;最后,将改进的粒子群优化算法应用于故障诊断,通过仿真研究和实验验证,对比几种算法的输出效果,评价了随机权重粒子群优化算法的有效性和优越性。

关键词: 分段势函数, 随机权重粒子群优化算法, 弱信号检测, 故障诊断

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

中图分类号:

O193

张金珂,张建刚. 基于改进粒子群优化算法的信号检测及故障诊断[J]. 《山东大学学报(理学版)》, 2023, 58(5): 63-75.

ZHANG Jinke, ZHANG Jiangang. Signal detection and fault diagnosis based on improved particle swarm optimization algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(5): 63-75.

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