考虑软件运行的软-硬件退化系统剩余寿命估计

Remaining Useful Life Estimation Model for Software-Hardware Deteriorating Systems with Software Operational Conditions

针对软-硬件系统级剩余寿命估计难题,传统的研究方法都是单独考虑了软件可靠性或硬件可靠性,而忽略了软件与硬件之间的交互影响。文中基于硬件性能退化过程提出了一种将软件的使用或运行看作是系统的一种外部冲击的新方法。该方法通过硬件性能退化指标来表征软件运行对系统的影响,主要采用离散隐Markov过程来描述两者之间的关系。具体地,对信号数据采用信号分解与特征提取技术得到性能退化指标,运用隐Markov模型构建隐含状态与实际退化之间的对应关系。根据在不同软件运行条件下系统性能退化指标样本中的拐点个数,对同一硬件退化过程分段构建不同的退化模型,使模型更加精确地描述退化过程。采用随机仿真技术与优化技术对硬件剩余寿命进行估计,根据系统体系结构估计软-硬件系统的剩余寿命。利用某武器装备系统的性能监测数据,将所提算法与传统系统级剩余寿命估计模型(BP神经网络)进行对比,证明了所提算法具有较高的估计精度。

For the estimation problem of theremaining useful life(RUL)of the software-hardware system-level,the traditional research methods consider software reliability or hardware reliability separately,and ignore the interaction effect between them.This paper proposed a new method of considering the use or operation of software as an external impact of the system based on the hardware performance degradation process.This method uses hardware performance degradation indicators to characterize the impact of software operations on the system.Discrete-time hidden Markov processes are mainly used to describe the relationship between them.Specifically,signal degradation and feature extraction techniques are applied to signal data to obtain performance degradation indicators.Hidden Markov models are used to construct the correspondence relation between implied states and actual degradation.According to the number of inflection points in the system performance degradation indicators under different software operating conditions,different degradation models are built on the same hardware degradation process,so that the model describesthe degradation process more accurately.Stochastic simulation technology and optimization technology are used to estimate,the RUL of the hardware,and according to the system architecture,the RUL of the software-hardware system is estimated.Using the performance monitoring data of a certain weapon equipment system,this paper compared the proposed algorithm with the traditional system-level RUL estimation model(BP neural network),and proved that the proposed algorithm has higher estimation accuracy.