Advanced Search
Volume 42 Issue 11
Nov.  2016
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(11): 2321-2331.
WANG Xin, WU Ji, LIU Chao, et al. Application of singular spectrum analysis to failure time series analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(11): 2321-2331. doi: 10.13700/j.bh.1001-5965.2015.0712(in Chinese)
Citation: WANG Xin, WU Ji, LIU Chao, et al. Application of singular spectrum analysis to failure time series analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(11): 2321-2331. doi: 10.13700/j.bh.1001-5965.2015.0712(in Chinese)
shu

Application of singular spectrum analysis to failure time series analysis

doi: 10.13700/j.bh.1001-5965.2015.0712
  • 1.

    School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. Xi'an Aeronautical Computing Technique Research Institute, Aviation Industry Corporation of China, Xi'an 710068, China

  • Received Date: 02 Nov 2015
  • Rev Recd Date: 29 Feb 2016
  • Publish Date: 20 Nov 2016
    Abstract
  • Due to significant industrial demands toward flight safety andairplane maintenance quality, improving airplane's reliability in usage stage has become an important activity and the research domain is rapidly evolving. In this paper, eighteen years' field data gathered from the maintenance phase of two Boeing 737 aircrafts are prepared as time-to-failure series. Then singular spectrum analysis (SSA) is usedto cope with this data for modeling and forecasting. Furthermore, a SSA parameter optimization algorithm is proposed by minimizing root mean square error (RMSE) of the prediction results. Based on this,a broader method of model combination is raised by utilizing different time series models to the components obtained from SSA decomposition, which represent trend, period, residuals, etc.The combination model and detailed algorithm are designed. The experimental results are compared with those of cubic exponential smoothing (Holt-Winters) and autoregressive integrated moving average (ARIMA), which verifies that the proposed models and algorithms have better fitting and prediction accuracyin failure time series analysis.

     

    • FullText(HTML)
  • loading
    • References(29)
  • [1]
    ROCCO S C M.Singular spectrum analysis and forecasting of failure time series[J].Reliability Engineering & System Safety,2013,114(6):126-136.
    [2]
    KUTY OWSKA M.Neural network approach for failure rate prediction[J].Engineering Failure Analysis,2015,47(1):41-48.
    [3]
    CAI K Y,CAI L,WANG W D,et al.On the neural network approach in software reliability modeling[J].Journal of Systems and Software,2001,58(1):47-62.
    [4]
    MOURA M D,ZIO E,LINS I D,et al.Failure and reliability prediction by support vector machines regression of time series data[J].Reliability Engineering & System Safety,2011,96(11):1527-1534.
    [5]
    FINK O,ZIO E,WEIDMANN U.Predicting component reliability and level of degradation with complex-valued neural networks[J].Reliability Engineering & System Safety,2014,121(1):198-206.
    [6]
    XU K,XIE M,TANG L C,et al.Application of neural networks in forecasting engine systems reliability[J].Applied Soft Computing,2003,2(4):255-268.
    [7]
    AL-GARNI A Z,JAMAL A.Artificial neural network application of modeling failure rate for Boeing 737 tires[J].Quality and Reliability Engineering International,2011,27(2):209-219.
    [8]
    GARCÍA F P,PEDREGAL D J,ROBERTS C.Time series methods applied to failure prediction and detection[J].Reliability Engineering & System Safety,2010,95(6):698-703.
    [9]
    BARBA L,RODRÍGUEZ N,MONTT C.Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents[J].Scientific World Journal,2014(1):152375.
    [10]
    VATUTARD R,YIOU P,GHIL M.Singular-spectrum analysis: A toolkit for short,noisy chaotic signals[J].Physica D:Nonlinear Phenomena,1992,58(1-4):95-126.
    [11]
    ELSNER J B,TSONIS A A.Singular spectrum analysis-A new tool in time series analysis[M].New York:Plenum Press,1996:39-50.
    [12]
    ZHANG Q,WANG B D,HE B,et al.Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting[J].Water Resources Management,2011,25(11):2683-2703.
    [13]
    王解先,连丽珍,沈云中.奇异谱分析在GPS站坐标监测序列分析中的应用[J].同济大学学报(自然科学版),2013,41(2):282-288.WANG J X,LIAN L Z,SHEN Y Z.Application of singular spectrum analysis to GPS station coordinate monitoring series[J].Journal of Tongji University(Natural Science),2013,41(2):282-288(in Chinese).
    [14]
    ELSNER J B.Analysis of time series structure: SSA and related techniques[J].Journal of the American Statistical Association,2002,103(4):1207-1208.
    [15]
    GOLYANDINA N,KOROBEYNIKOV A.Basic singular spectrum analysis and forecasting with R[J].Computational Statistics & Data Analysis,2014,71(s1):934-954.
    [16]
    GOLYANDINA N.On the choice of parameters in singular spectrum analysis and related subspace-based methods[J].Statistics and Its Interface,2010,3(3):259-279.
    [17]
    HIPEL K W,MCLEOD A I.Time series modelling of water resources and environmental systems[M].Amsterdam: Elsevier,1994:893.
    [18]
    HASSANI H.Singular spectrum analysis:Methodology and comparison[J].Journal of Data Science,2007,5:239-257.
    [19]
    ALEXANDROV T,GOLYANDINA N.Automatic extraction and forecast of time series cyclic components within the framework of SSA[C]//Proceedings of the 5th Workshop on Simulation,2005:45-50.
    [20]
    WINTERSP R.Forecasting sales by exponentially weighted moving averages[M]//FUNKE U H.Mathematical models in marking.New York:Springer,1976:384-386.
    [21]
    HOLT C C.Forecasting seasonals and trends by exponentially weighted moving averages[J].International Journal of Forecasting,2004,20(1):5-10.
    [22]
    CHATFIELD C.The Holt-Winters forecasting procedure[J].Journal of the Royal Statistical Society,1978,27(3):264-279.
    [23]
    BOX G E P,JENKINS G M,REINSEL G C.Time series analysis:Forecasting and control (revised edition)[J].Journal of Marketing Research,1976,22(2)：199-201.
    [24]
    KUMAR U,JAIN V K.ARIMA forecasting of ambient air pollutants (O3,NO,NO2 and CO)[J].Stochastic Environmental Research and Risk Assessment,2010,24(5):751-760.
    [25]
    JEONG K,KOO C,HONG T.An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)[J].Energy,2014,71(14):71-79.
    [26]
    HYNDMAN R J,KHANDAKAR Y.Automatic time series forecasting: The forecast package for R[J].Journal of Statistical Software,2008,27(3):1-22.
    [27]
    EBELING C E.An introduction to reliability and maintainability engineering[M].New York:McGraw-Hill,1997.
    [28]
    SU C,JIN Q,FU Y.Correlation analysis for wind speed and failure rate of wind turbines using time series approach[J].Journal of Renewable and Sustainable Energy,2012,4(3):1-13.
    [29]
    LI G,SHI J.On comparing three artificial neural networks for wind speed forecasting[J].Applied Energy,2010,87(7):2313-2320.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(1008) PDF downloads(734) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return