A fault diagnosis method with application of HMM Based onadaptive particle swarm optimization

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (20) : 264-270.

GUO Sen1, WANG Dawei1, ZHANG Shaowei1, ZHANG Xuecheng2

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Abstract

Aiming at the problem that parameters learning algorithm of hidden Markov model (HMM) easily converges to local optimal solutions, an improved HMM was proposed based on adaptive particle swarm optimization (PSO).An inertial weight factor was joined in the basic PSO.It was controlled dynamically based on the iterative results of the algorithm.Global search capability of the new algorithm thus was improved.The adaptive method was integrated to parameters learning algorithm of HMM, which contributed to optimize the initial parameters of HMM.The new model was applied to fault diagnosis for diesel engine with normal state, lack of air supply and intake valve clearance fault.Actual engine vibration data were analyzed.The correct classification rate of the proposed method reaches 97.3%, better than the traditional method based on PSO.The results verify that the proposed fault diagnosis method has a better performance than the traditional algorithm.

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particle swarm optimization / adaptive method / hidden Markov model / fault diagnosis

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GUO Sen, WANG Dawei, ZHANG Shaowei, ZHANG Xuecheng. A fault diagnosis method with application of HMM Based onadaptive particle swarm optimization[J]. Journal of Vibration and Shock, 2021, 40(20): 264-270

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