A novel methodology based on hidden semi-Markov model for equipment health assessment

摘要：

　　As one of the most important aspects of PHM in many application domains, health monitoring and management could maximize the equipment effectiveness within the allowed health ranges.This paper proposes a novel approach to assess the equipment health based on hidden semi-Markov model (HSMM), which is an extension of HMM and does not follow the unrealistic Markov chain assumption to provide more powerful modeling and analysis capability for real problems.With training the standard health state HSMM model by normal state data, the test data is inputted into the trained model in order to calculate the corresponding relative divergence, which is the deviation extent from the standard health state model.Then we can obtain the health index model for the equipment health monitoring and measurement.Moreover, the proposed HSMM based method is applied to the draught fan and showed to be effective.

关键词： health assessment HSMM forward-backward algorithm Kullback-Leibler divergence

作者: Huo Lin Fei Simiao Lv Chuan Wang Zili

作者单位: School of Safety Engineering, Shenyang Aerospace University, Shenyang, China Shenyang Aircraft Design and Research Institute, Shenyang, China Reliability and System Engineering Department, Beihang University, China

会议类型: 国际会议

会议名称: the International Conference Vibroengineering-2014

会议地点: 贵阳

会议语种:英文

页码: 271-276

在线出版日期: 2014-11-07（万方平台首次上网日期，不代表论文的发表时间）