基于综合健康指数与RVM的系统级失效预测

System-level failure prognostics using synthesized health index and relevance vector machine

针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。

For complicated engineering systems with multiple health indicators,multiple operation and fault modes,a system-level failure prediction method is presented based on the synthesized health index （SHI）and the rele-vance vector machine （RVM）.In the offline training phase,the health assessment models for each operation mode are firstly developed using historical data,which then will be utilized to calculate the corresponding SHI sequences for each degradation path.Moreover,the model that has the best fit to the historical SHI sequences is selected with the help of RVM regression.In the online prediction phase,the parameters of the selected model are estimated and updated using the online SHI sequences and the RVM,then time-varying noises are also added to the selected model to represent the uncertainty.Further,the probability density distribution of system remaining useful life is obtained by the model ex-trapolating in time.The method is successfully applied to the failure prediction of turbine engines.