1,380
Views
52
CrossRef citations to date
0
Altmetric
Quality & Reliability Engineering

Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach

&
Pages 853-867 | Received 13 Aug 2017, Accepted 04 Feb 2018, Published online: 17 May 2018

References

  • Bae, S.J., Kuo, W. and Kvam, P.H. (2007) Degradation models and implied lifetime distributions. Reliability Engineering and System Safety, 92(5), 601–608.
  • Bae, S.J. and Kvam, P.H. (2004) A nonlinear random-coefficients model for degradation testing. Technometrics, 46(4), 460–469.
  • Bagdonavicius, V. and Nikulin, M.S. (2001) Estimation in degradation models with explanatory variables. Lifetime Data Analysis, 7(1), 85–103.
  • Baraldi, P., Mangili, F. and Zio, E. (2012) A Kalman filter-based ensemble approach with application to turbine creep prognostics. IEEE Transactions on Reliability, 61(4), 966–977.
  • Brotherton, T., Grabill, P., Wroblewski, D., Friend, R., Sotomayer, B. and Berry, J. (2002) A testbed for data fusion for engine diagnostics and prognostics, in Proceedings of IEEE Aerospace Conference 2002, IEEE Press, Piscataway, NJ, pp. 3029–3042.
  • Chapelle, O., Schölkopf, B. and Zien, A. (2006) Semi-Supervised Learning, MIT Press, Cambridge, MA.
  • Chen, N. and Tsui, K.L. (2013) Condition monitoring and remaining useful life prediction using degradation signals: Revisited. IIE Transactions, 45(9), 939–952.
  • Christer, A.H., Wang, W. and Sharp, J.M. (1997) A state space condition monitoring model for furnace erosion prediction and replacement. European Journal of Operation Research, 101(1), 1–14.
  • Fang, X., Gebraeel, N. and Paynabar, K. (2017) Scalable prognostic models for large-scale condition monitoring applications. IISE Transactions, 49(7), 698–710.
  • Gebraeel, N. Z. (2006) Sensor-updated residual life distributions for components with exponential degradation patterns. IEEE Transactions on Automation Science and Engineering, 3(4), 382–393.
  • Gebraeel, N.Z., Lawley, M.A., Li, R. and Ryan, J.K. (2005) Resitual-life distributions from component degradation signals: A Bayesian approach. IIE Transactions, 37(6), 543–557.
  • Hall, D.L. and Llinas, J. (1997) An introduction to multisensor data fusion. Proceedings of the IEEE, 85, 6–23.
  • Hastie, T., Tibshirani, R. and Friedman, J.H. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition, Springer, New York, NY.
  • Hong, Y., Duan, Y., Meeker, W.Q., Stanley, D.L. and Gu, X. (2015) Statistical methods for degradation data with dynamic covariates information and an application to outdoor weathering data. Technometrics, 57(2), 180–193.
  • Hu, C., Youn, B.D., Wang, P. and Yoon, J.T. (2012) Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering and System Safety, 103, 120–135.
  • Jardine, A.K., Lin, D. and Banjevic, D. (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
  • Koenker, R. (2005) Quantile Regression, Cambridge University Press, Cambridge, UK.
  • Lawless, J. and Crowder, M. (2004) Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Analysis, 10(3), 213–227.
  • Liu, K., Chehade, A. and Song, C. (2017) Optimize the signal quality of the composite health index via data fusion for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 14(3), 1504–1514.
  • Liu, K., Gebraeel, N.Z. and Shi, J. (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 10(3), 652–664.
  • Liu, K. and Huang, S. (2016) Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Transactions on Automation Science and Engineering, 13(1), 344–354.
  • Liu, K. and Shi, J. (2015) Internet of things (IoT)-enabled system informatics for service decision making: Achievements, trends, challenges, and opportunities. IEEE Intelligent Systems, 30(6), 18–21.
  • Loutas, T.H., Roulias, D. and Georgoulas, G. (2013) Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic e-support vectors regression. IEEE Transactions on Reliability, 62(4), 821–832.
  • Lu, C.J. and Meeker, W.O. (1993) Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161–174.
  • Lu, C.J., Park, J. and Yang, Q. (1997) Statistical inference of a time-to-failure distribution derived from linear degradation data. Technometrics, 39(4), 391–400.
  • Meeker, W.Q. and Escobar, L.A. (1998) Statistical Methods for Reliability Data, Wiley, New York, NY.
  • Nelson, W. (1990) Accelerated Testing Statistical Models, Test Plans and Data Analysis, Wiley, New York, NY.
  • Nocedal, J. and Wright, S. (2006) Numerical Optimization, Springer, New York, NY.
  • Oberhofer, W. (1982) The consistency of nonlinear regression minimizing the L1-norm. The Annals of Statistics, 10(1), 316–319.
  • Park, J.I. and Bae, S.J. (2010) Direct prediction methods on lifetime distribution of organic light-emitting diodes from accelerated degradation tests. IEEE Transactions on Reliability, 59(1), 74–90.
  • Saha, B., Goebel, K. and Christophersen, J. (2009) Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control, 31(3–4), 293–308.
  • Sarkar, S., Jin, X. and Ray, A. (2011) Data-driven fault detection in aircraft engines with noisy sensor measurements. Journal of Engineering for Gas Turbines and Power, 133(8), 081602.
  • Saxena, A. and Goebel, K. (2008) Turbofan engine degradation simulation data set, NASA Ames Research Center, Moffett Field, CA. Available at: (https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository) [Accessed 30 April 2018].
  • Saxena, A., Goebel, K., Simon, D. and Eklund, N. (2008) Damage propagation modeling for aircraft engine run-to-failure simulation, in Proceedings of the International Conference on Prognostics and Health Management 2008, IEEE, Denver, CO, pp. 1–9.
  • Si, X.S., Wang, W., Hu, C.H. and Zhou, D.H. (2011) Remaining useful life estimation—A review on the statistical data driven approaches. European Journal of Operation Research, 213(1), 1–14.
  • Si, X.S., Wang, W., Hu, C.H., Zhou, D.H. and Pecht, M.G. (2012) Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Transactions on Reliability, 61(1), 50–67.
  • Tian, Z. (2012) An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing, 23(2), 227–237.
  • Vaisnys, P., Contri, P., Rieg, C. and Bieth, M. (2006) Monitoring the effectiveness of maintenance programs through the use of performance indicators. Report EUR 22602, DG JRC – Institute for Energy, Petten, The Netherlands.
  • Wang, T., Yu, J., Siegel, D. and Lee, J. (2008) A similarity-based prognostics approach for remaining useful life estimation of engineered system, in Proceedings of the International Conference on Prognostics and Health Management 2008, IEEE, Denver, CO.
  • Wang, X. and Xu, D. (2010) An inverse Gaussian process model for degradation data. Technometrics, 52(2), 188–197.
  • Whitmore, G.A. and Schenkelberg, F. (1997) Modelling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Analysis, 3(1), 27–45.
  • Xu, Z., Ji, Y. and Zhou, D. (2008) Real-time reliability prediction for a dynamic system based on the hidden degradation process identification. IEEE Transactions on Reliability, 57(2), 230–242.
  • Yang, F., Habihullah, M.S., Zhang, T., Xu, Z., Lim, P. and Nadarajan, S. (2016) Health index-based prognostics for remaining useful life predictions in electrical machines. IEEE Transactions on Industrial Electronics, 63(4), 2633–2644.
  • Ye, Z.S. and Chen, N. (2014) The inverse Gaussian process as a degradation model. Technometrics, 56(3), 302–311.
  • Ye, Z.S., Wang, Y., Tsui, K.L. and Pecht, M. (2013) Degradation data analysis using Wiener process with measurement errors. IEEE Transactions on Reliability, 62(4), 772–780.
  • Ye, Z.S., and Xie, M. (2015) Stochastic modeling and analysis of degradation for highly reliable products. Applied Stochastic Models in Business and Industry, 31(1), 16–32.
  • Yu, H. (2006) Designing an accelerated degradation experiment with a reciprocal Weibull degradation rate. Journal of Statistical Planning and Inference, 136(1), 282–297.
  • Zhai, Q. and Ye, Z.S. (2017) RUL prediction of deteriorating products using an adaptive Wiener process model. IEEE Transactions on Industrial Informatics, 13(6), 2911–2921.
  • Zhou, Q., Son, J., Zhou, S., Mao, X. and Salman, M. (2014) Remaining useful life prediction of individual units subject to hard failure. IIE Transactions, 46(10), 1017–1030.
  • Zhou, R., Serban, N. and Gebraeel, N. (2014) Degradation-based residual life prediction under different environments. The Annals of Applied Statistics, 8(3), 1671–1689.
  • Zhou, R., Serban, N., Gebraeel, N. and Müller, H.G. (2014) A functional time warping approach to modeling and monitoring truncated degradation signals. Technometrics, 56(1), 67–77.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.