In this paper,an algorithm combining kernel principal component analysis(KPCA),osprey cauchy sparrow search algorithm(OCSSA)and kernel extreme learning machine(KELM)is proposed to solve the problem of non-Gaussian data and strong data coupling in the process of chemical industry.Firstly,the KPCA algorithm is employed for dimensionality reduc-tion,followed by the utilization of the OCSSA algorithm to determine the optimal values for kernel parameter γ and regular-ization coefficient C in KELM.Enhancements made to the sparrow algorithm include:augmenting population diversity through incorporating chaotic mapping technology,substituting the original sparrow algorithm's finder position update formula with a global exploration strategy derived from Osprey optimization,and replacing the follower position update formula of the origi-nal sparrow algorithm with Cauchy variation strategy.Ultimately,a bearing fault diagnosis and classification algorithm that in-tegrates KPCA,OCSSA,and KELM algorithms is established.Experimental results demonstrate that after undergoing OCSSA optimization,this approach exhibits remarkable accuracy and effectiveness in addressing bearing faults.
关键词
故障诊断/核主成分分析/麻雀算法/核极限学习机
Key words
fault diagnosis/kernel principal component analysis/sparrow algorithm/kernel extreme learning machine
维普
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