基于改进退化隐马尔科夫模型的设备健康诊断与寿命预测研究

In order to solve the problem of large deviation between Hidden Markov Model and actual equipment health diagnosis, this paper developed an improved Degenerated Hidden Markov Model (DGHMM) with a core of the quasi power relation. First, the model adopted the degradation factors, modeling the process of recession for the equipments continuous decrease in performance. Compared with the conventional exponential accelerated degradation, the quasi power relation accelerated degradation can better describe the process that the performance of the equipment decreases gradually with the increase of service age. Then, the improved genetic algorithm can replace the conventional EM algorithm for parameters estimation, which overcomes the limitation that the EM algorithm is easy to fall into local optimization. At the same time, in terms of the limitation of life prediction problem as a result of the Hidden Markov Model must obey exponential distribution, an algorithm named greed & approximation based on approximation algorithm and Viterbi algorithm came out, and to seek maximum probability remaining observation, for the purpose of seeking maximum probability dynamically surplus state path, to predict the residual life of equipment. Finally, the proposed method is validated and evaluated with the data set of caterpillar hydraulic pumps. The results show that the method of equipment health diagnosis and life prediction based on the improved degraded hidden Markov model is more effective in describing equipments degeneration and the accuracy of equipment state diagnosis, and is also feasible in the prediction of residual life.