基于LCLSABO-KELM滚动轴承故障诊断方法研究

Research on fault diagnosis method of rolling bearing based on LCLSABO-KELM

为提高滚动轴承的故障诊断的分类精度,提出一种逻辑斯蒂柯西莱维减法平均优化(Logistic-Cauchy-Levy-subtraction average-based optimization,LCLSABO)算法,该算法融合了混沌(Logistic)映射策略、柯西(Cauchy)变异策略和莱维(Levy)飞行策略,用以优化核极限学习机(kernel extreme learning machine,KELM)的性能。首先,利用混沌映射策略优化减法平均优化算法的种群初始化,增强种群多样性;其次,结合柯西变异策略与莱维飞行策略,改进位移算法,提高全局搜索能力,有效避免陷入局部最优解;最后,采用LCLSABO算法优化KELM的核心参数,建立LCLSABO-KELM模型,对轴承故障进行分类与诊断。试验结果表明,与SABO-KELM模型、SSA-KELM模型、PSO-KELM模型及传统KELM模型相比,LCLSABO-KELM模型的故障诊断分类精度为98.63%,分别提升了0.97%、2.70%、3.90%和11.30%。这表明,该方法能够充分提取故障特征,显著提高故障诊断的分类精度,验证了该模型在滚动轴承故障诊断与分类中的优越性能。

To improve the classification accuracy of rolling bearing fault diagnosis,an Logistic-Cauchy-Levy-subtraction average-based optimization(LCLSABO)algorithm was proposed.The algorithm combines Logistic mapping,Cauchy mutation strategy and Levy flight strategy to optimize the performance of the kernel extreme learning machine(KELM).Firstly,the Logistic mapping is used to optimize the population initialization in the subtraction-average-based optimization algorithm to enhance the population diversity.Secondly,Cauchy variation strategy and Levy flight strategy are incorporated to refine the displacement mechanism of the algorithm,improving global search capabilities and effectively avoiding local optima.Finally,the key parameters of KELM were optimized by the LCLSABO algorithm,and the LCLSABO-KELM model was established to classify and diagnose bearing faults.Experimental results show that compared with the SABO-KELM model,SSA-KELM model,PSO-KELM model and traditional KELM model,the fault diagnosis classification accuracy of the LCLSABO-KELM model is 98.63%,which is improved by 0.97%,2.70%,3.90%,and 11.30%,respectively.This demonstrates that the proposed method effectively extracts fault features and significantly enhances the classification accuracy of fault diagnosis,demonstrating its superior performance in rolling bearing fault diagnosis and classification.