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.
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