Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 511-517.

• Interdiscipline & Application • Previous Articles     Next Articles

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

HAN Jia-jia, ZHANG De-ping   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics & Astronautics,Nanjing 210016,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: 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 technologyare 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.

Key words: Software-Hardware system, Remaining useful life estimation, Discrete-time hidden Markov process, Degradation model

CLC Number: 

  • TP311
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