摘要
针对设备运行状态识别与故障预测问题,提出一种基于时变转移概率的隐半Markov模型。该模型将设备历史运行信息融入Markov状态转移概率矩阵的估计过程中,使Markov状态转移概率矩阵具有时变特性。基于改进前向后向算法研究了相应的隐半Markov模型参数估计方法,使其能够不断综合利用历史运行信息进行自我更新,以更加符合设备真实运行的过程。同时以该模型为基础,利用故障率方法建立了对设备剩余使用寿命进行预测的基本步骤。通过某滚动轴承运行状态识别实例演示了该模型的建模过程,证明了基于该模型的设备状态识别与预测方法比传统隐半Markov模型方法更为有效。
Aiming at the problem of equipment operation state identification and fault prognosis, a Duration-Depend- ent Hidden SemFMarkov ModeI（DD-HSMM）was proposed. In this model, the historical operation information was merged into estimation process of Markov state transition probability matrix, thus the matrix had time variant char- acteristics. Furthermore, the parameter estimation method of Hidden Semi-Markov Model（HSMM）was studied based on improved forward-backward algorithm to make self-renewal by using historical operation information. The basic steps for predicting the Remaining Useful Life（RUL）of equipment was built by using fault rate method. Through a case of a rolling hearing＇s operation state to demonstrate the modeling process of proposed model, and the result showed that the proposed method was more effective than traditional HSMM model.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2012年第8期1861-1868,共8页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(71101116)
航空科学基金资助项目(2009ZE53052)
陕西省科技计划资助项目(2010K8-11)~~
关键词
时变转移概率
隐半Markov模型
故障率
状态识别
剩余有效寿命
： duration-dependent state transition probabilities
hidden semi-Markov model
hazard rate
state recogni-tiom remaining useful life
