Residual useful life prognosis of equipment based on modified hidden semi-Markov model with a co-evolutional optimization method

Abstract

Health diagnosis and prognosis of equipment is considered crucial for condition-based maintenance. This paper presents a prognosis health and monitoring scheme framework based on the modified hidden semi-Markov model (HSMM). First, a novel scalarization method for raw signals is developed based on the Weibull distribution with a short-time function window. Then, the conglutination coefficient and deterioration kernel are introduced into HSMM to simulate the inherent deterioration process of equipment. A co-evolutional algorithm based on genetic algorithm and salp swarm algorithm is proposed to estimate the parameters of the modified HSMM. A model base is established based on various sub-models of different faults for the health prognosis and diagnosis of equipment. The residual useful life can be calculated based on the historical whole useful life (WUL) data and the real-time status of equipment. Finally, the feasibility and effectiveness of the proposed model is verified on the turbofan engine dataset. The proposed scheme can provide a novel solution for the health prognosis of the non-vibration signal.