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

• Published in: Computers & industrial engineering Vol. 182; p. 109433
• Main Authors: Liu, Qinming, Liu, Wenyi, Dong, Ming, Li, Zhinan, Zheng, Yihan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Co-evolutional algorithm; Health prognosis; Hidden semi-Markov model; Machine learning; Residual useful life; Hidden semi-Markov model; Health prognosis; Residual useful life; Machine learning; Co-evolutional algorithm
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2023.109433

•Health prognostics scheme based on modified hidden semi-Markov model is proposed.•The proposed scalarization method can process non-vibration signal data well.•Proposed method is effective in prognostics health management of complex systems.•Residual useful life of equipment is predicted using historical whole useful life data. 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.