Equipment health diagnosis and prognosis using hidden semi-Markov models

In this paper, the development of hidden semi-Markov models (HSMMs) for equipment health diagnosis and prognosis is presented. An HSMM is constructed by adding a temporal component into the well-defined hidden Markov model (HMM) structures. The HSMM methodology offers two significant advantages over the HMM methodology in equipment health diagnosis and prognosis: (1) it overcomes the modeling limitation of HMM due to the Markov property and therefore improves the power in diagnosis, and (2) it can be directly used for prognosis. The application of the HSMMs to equipment health diagnosis and prognosis is demonstrated with the fault classification application of UH-60A Blackhawk main transmission planetary carriers and prognosis of a hydraulic pump health monitoring application. The effectiveness of the HSMMs is compared with that of the HMMs. The results of the application testing have shown that the HSMMs are capable of identifying the faults under both test cell and on-aircraft conditions while the performance of the HMMs is not comparable with that of the HSMMs. Furthermore, the HSMM-based methodology can be used to estimate the remaining useful life of equipment.