计算机技术与发展Issue(1)：184-187,191,5.DOI:10.3969/j.issn.1673-629X.2014.01.047
隐半马尔可夫模型在剩余寿命预测中的应用
Application of Hidden Semi-Markov Model in Prediction of Residual Life
摘要
Abstract
Prediction of equipment residual life based on the recognition of degradation is the important aspect in a condition-based main-tenance which indeed actualizes the maintenance in a proper time. As a statistic analysis algorithm,the Hidden Markov Model ( HMM) with well capability in pattern classification has a successful application in identification of equipment degradation state. But HMM cannot be directly used to prognosticate residual life. In this paper,considering the limitations of HMM and the explanation of remaining life pre-diction model,apply the Hidden Semi-Markov Model ( HSMM) for modeling and forecasting. In view of problems that HSMM training algorithm can easily fall into local extreme point,the algorithm based on Particle Swarm Optimization ( PSO) is proposed to improve. Ex-perimental results show that the method on the residual life prediction of equipment has effectiveness and feasibility.关键词
隐半马尔可夫模型/微粒群优化算法/剩余寿命/预测Key words
hidden simi-Markov model ( HSMM)/particle swarm optimization ( PSO)/residual life/forecast分类
信息技术与安全科学引用本文复制引用
原媛,卓东风..隐半马尔可夫模型在剩余寿命预测中的应用[J].计算机技术与发展,2014,(1):184-187,191,5.基金项目
国家自然科学基金资助项目(41272374) （41272374）
