关键词: 高阶隐半马尔科夫模型, 复合节点, 模型降阶, 状态驻留, 多项式拟合, 剩余寿命预测

Abstract: To solve the problem of large residual life prediction error,a residual life prediction model based on Higher-order Hidden Semi Markov Model (HOHSMM) was proposed.Based on Hidden Semi Markov Model,HOHSMM was established,and an order reduction method was proposed based on permutation and composite nodes mechanism,accordingly improve the state transition matrix and the observation matrix,making the higher order model be converted into the corresponding first-order model.Thus there would be more nodes dependency information storing in the parameters to be estimated.Estimation Maximal algorithm was replaced by intelligent optimization algorithm group to estimate the parameters and optimize the structure of the model,which simplified the topology of the high-order model.T he state lingering variable in the higher-order model were defined and derived,and the prediction method based on polynomial fitting was used to realize the prediction of equipment residual life in the case of unknown prior distribution.The framework was validated by the hydraulic pump data set of Caterpillar Inc.The results showed that the residual life prediction method based on the High-order Hidden Semi Markov Model was more effective.

Key words: higher-order hidden semi Markov model, composite nodes, model order reduction, state lingering, polynomial fitting, residual useful life