Research on Health Assessment Method of Equipment Based on Hidden Markov Fusion Mechanism

Health State Assessment (HSA) is foundation of predictive maintenance which is of great significance to prevent catastrophic faults of equipment especially of aeronautical and space equipment. To address the problem that life cycle data based methods are deeply depend on a great amount of data and physics of failure based methods are strongly related to the accuracy of the model, taking the highly consistent between health state-observable information and Hidden Markov (HM), a novel HM Fusion Mechanism (HM-FM) based HAS method is proposed. The intension and essence are provided firstly, and then, the process and algorithm of HM are presented in detail. Furthermore, an optimal HM-FM based HAS system is construct which includes requirements analysis, fusion degree degradation assessment and self-adjusting alarm threshold setting. Finally, aircraft motor bearings are presented to demonstrate the proposed model, application analysis shows the proposed model is feasible and effective, and this approach can be used for HAS of other equipment.