摘要
针对航空发动机的突发故障,提出了一种基于多状态混合高斯隐马尔科夫模型(mixture of Gaussian-hidden Markov model,简称MOG-HMM)和Viterbi算法相结合的预测方法。首先,根据航空发动机突发故障的历史监测数据建立多状态MOG-HMM模型,确定状态数、状态转移矩阵、观察值概率分布以及最终的突发故障状态;然后,对新采集的观测数据,通过Viterbi算法解码出该观测数据对应的当前状态;最后,计算该状态到达突发故障状态的时间间隔,从而可以对突发故障进行预测。仿真和实验结果表明,该方法能够实现对突发故障的预测,并且符合标准预测指标的要求。
Aiming at abrupt failure of aero-engine,an aero-engine abrupt failure prognosis method is proposed by combining of multi-states MOG-HMM and Viterbi algorithm.First of all,according to the historical monitoring data of aero-engine,a multi-states MOG-HMM model is built,that it can determine the number of states,the state transfer matrix,the observation probability distribution and the ultimate abrupt failure state.Then new observation data are collected,through Viterbi algorithm to decode the data to obtain its current state.Finally,the abrupt failure can be predicted by by calculating the time from current state to the final abrupt failure state.Simulation and experimental results show that the method can realize the prognosis of abrupt failure,which also meet the requirements of standard predictor.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2014年第2期310-314,399,共5页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51075330
50975231)
陕西省自然科学基础研究计划资助项目(2013JM7011)
航空科学基金资助项目(20132153027)
