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
[1] DU D,HU C,SI X,et al.An improved remaining useful life prediction method for system with volatile degradation path[C]∥Prognostics and System Health Management Conference.IEEE,2017:1-5.
[2] DANGBO D U,CHANGHUA H U,XIAOSHENG S I,et al.Remaining Useful Life Prediction for Hybrid Degradation System[J].Journal of Shanghai Jiaotong University,2017,51(7):886-891.
[3] ZHANG H,CHEN M,ZHOU D.Predicting remaining useful life for a multi-Component system with public noise[C]∥Prognostics and System Health Management Conference.IEEE,2017:1-6.
[4] BAPTISTA M,HENRIQUES E P,DEMEDEIROS I,et al.Remaining Useful Life Estimation in Aeronautics:Combining data-driven and Kalman filtering[J].Reliability Engineering & System Safety,2018,184:228-239.
[5] WANG H K,LI Y F,LIU Y,et al.Remaining useful life estimation under degradation and shock damage[C]∥Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk &Reliability.2017.
[6] ZHANG H,CHEN M,ZHOU D.Remaining useful life prediction for nonlinear degrading systems with maintenance[C]∥Prognostics and System Health Management Conference.2017:1-5.
[7] TENG X,PHAM H,DANIEL R.Reliability modeling of hardware and software interactions,and its application[C]∥IEEE Transaction on Reliability.2006:571-577.
[8] TUMER I,SMIDTS C.Integrated design-stage failure analysis of software-driven hardware systems[C]∥IEEE Transactions on Computers.2010:1072-1084.
[9] GOEL A L,OKUMOTO K.A Markovian model for reliability and other performance measures of software systems[C]∥Proceedings of the National Computer Conference.1979:769-774.
[10] PARZEN E.Stochastic Processes[M].San Francisco,CA:Holden-Day,1962.
[11] HUANG W.Reliability analysis considering product perform-ance degradation[D].Tucson,Arizona:The University of Arizona,2002.
[12] TENG X,PHAM H,JESKE D R.Reliability modeling of hardware and software interactions,and its applications[J].Microcomputer & Its Applications,2011,55(4):571-577.
[13] WELKE S R,JOHNSON B W,AYLOR J H.Reliability mode-ling of hardware/software systems[J].IEEE Trans.Reliability,1995,44(3):413-418.
[14] HECHT HHECHT M.Software reliability in the system context[J].IEEE Transactions on Software Engineering,1986,12(1):51-58.
[15] SANKARARAMAN S,GOEBEL K.Why is the remaining useful life predictionuncertain[C]∥Annual Conference of the Prognostics and Health Management Society.2013:1-13.
[16] SANKARARAMAN S,DAIGLE M J,GOEBEL K.Uncertainty quantification inremaining useful life prediction using first-order reliability methods,Reliability[J].IEEE Transaction on Reliability,2014,63(2):603-619.
[17] JOUIN M,GOURIVEAU R,HISSEL D,et al.Degradations-analysis and aging modeling for health assessment and prognostics of PEMTF[J].Reliability Engineering and System Safety,2016,148:78-95.
[18] ZHANG Q,TSE P W T,WAN X,et al.Remaining usefullife estimation for mechanical systems based on similarity ofphase space trajectory[J].Expert Systems with Applications,2015,42(5):2353-2360.
[19] PARK J,KIM H J,SHIN J H,et al .Anembedded software re-liability model with consideration ofhardware related software failures [C]∥IEEE Sixth InternationalConference on Software Security and Reliability.2012:207-214 .
[20] TOKUNO K,YAMADA S.Codesign-oriented performability-modeling for hardware-software systems [J].IEEE Transactionson Reliability,2011,60(1):171-179 .
[21] PADGETT W J,TOMLINSON M A.Inference from accelerated degradation and failure data based on Gaussian process models[J].Lifetime Data Analysis,2004,10:191-206.
[22] KHAROUFEH J P,COX S M.Stochastic models for degradation-based reliability[J].IIE Transactions,2005,37(6):533-542.
[23] TSENG S T,PENG C Y.Stochastic diffusion modeling of degradation data[J].Journal of Data Science,2007,5(3):315-333.
[24] PARK C,PADGETT W J.Stochastic degradation models with several accelerating variables[J].IEEE Transactions on Reliabi-lity,2006,55(2):379-390.
[25] PARK J,KIM H J,SHIN J H,et al.An Embedded Software Reliability Model with Consideration of Hardware related Software Failures [C]∥IEEE Sixth International Conference on Software Security and Reliability.2012:207-214.
[26] 孟永鹏,贾申利,荣命哲.小波包频带能量分解在断路器机械状态监测中的应用[J].西安交通大学学报,2004(10):1013-1017.
[27] ACADEMIC,YANG Y,DEJIE Y U,CHENG J,et al.Application of Emprical Mode Decomposition (EMD) in Roller Bearing Fault Diagnosis[J].Journal of Hunan University,2003(5):132-138.
[28] DURAND J B,GAUDOIN O.Software Reliability Modelling and Prediction with Hidden Markov Chain[J].Statistical Modelling,2006,5(1):75-93.
[29] 刘河生,高小榕,杨福生.隐马尔可夫模型的原理与实现[J].国际生物医学工程杂志,2002,25(6):253-259.
[30] 奚立峰,黄润青,李兴林,等.基于神经网络的球轴承剩余寿命预测[J].机械工程学报,2007(10):137-143.
[1] XIAO Su,HAN Guo-qiang,WO Yan. Survey of Digital Image Super Resolution Reconstruction Technology [J]. Computer Science, 2009, 36(12): 8-13.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .