A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis. (December 2015)

• Record Type: Journal Article
• Authors: Liu, Qinming; Dong, Ming; Lv, Wenyuan; Geng, Xiuli; Li, Yupeng
• Is Part Of: Mechanical systems and signal processing. Volume 64/65(2015)
• Page Start: 217
• Page End: 232
• Publication Date: 2015-12
• Subjects: Prognosis -- Monitoring -- Hidden semi-Markov model -- Adaptive training -- Remaining useful lifetime; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.03.029
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Health prognosis for equipment is considered as a key process of the condition-based maintenance strategy. This paper presents an integrated framework for multi-sensor equipment diagnosis and prognosis based on adaptive hidden semi-Markov model (AHSMM). Unlike hidden semi-Markov model (HSMM), the basic algorithms in an AHSMM are first modified in order for decreasing computation and space complexity. Then, the maximum likelihood linear regression transformations method is used to train the output and duration distributions to re-estimate all unknown parameters. The AHSMM is used to identify the hidden degradation state and obtain the transition probabilities among health states and durations. Finally, through the proposed hazard rate equations, one can predict the useful remaining life of equipment with multi-sensor information. Our main results are verified in real world applications: monitoring hydraulic pumps from Caterpillar Inc. The results show that the proposed methods are more effective for multi-sensor monitoring equipment health prognosis. Highlights: Multi-sensor monitoring equipment health prognosis is analyzed. Adaptive hidden semi-Markov model is proposed for health prognosis. The proposed model and hazard rate equations are used to predict RUL. The performance of the proposed methods by one case study is analyzed. The proposed methods have better performance than other methods.