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Data classification and MTBF prediction with a multivariate analysis approach

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  • Braglia, Marcello
  • Carmignani, Gionata
  • Frosolini, Marco
  • Zammori, Francesco

Abstract

The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data.

Suggested Citation

  • Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    • Handle: RePEc:eee:reensy:v:97:y:2012:i:1:p:27-35
    DOI: 10.1016/j.ress.2011.09.010
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    References listed on IDEAS

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    1. Samrout, M. & Châtelet, E. & Kouta, R. & Chebbo, N., 2009. "Optimization of maintenance policy using the proportional hazard model," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 44-52.
    2. Bebbington, Mark & Lai, Chin-Diew & Zitikis, RiÄ ardas, 2007. "A flexible Weibull extension," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 719-726.
    3. Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    4. Sander, Peter & Wang, Wenbin, 2000. "Maintenance and reliability," International Journal of Production Economics, Elsevier, vol. 67(1), pages 1-2, August.
    5. Lu, Yuan & Loh, Han Tong & Brombacher, Aarnout Cornelis & Ouden, Elke den, 2000. "Accelerated stress testing in a time-driven product development process," International Journal of Production Economics, Elsevier, vol. 67(1), pages 17-26, August.
    6. D Lin & D Banjevic & A K S Jardine, 2006. "Using principal components in a proportional hazards model with applications in condition-based maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(8), pages 910-919, August.
    7. You, Ming-Yi & Li, Hongguang & Meng, Guang, 2011. "Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 590-598.
    8. Tian, Zhigang & Liao, Haitao, 2011. "Condition based maintenance optimization for multi-component systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 581-589.
    9. J Ansell & T Archibald & J Dagpunar & L Thomas & P Abell & D Duncalf, 2003. "Analysing maintenance data to gain insight into systems performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(4), pages 343-349, April.
    10. Waeyenbergh, Geert & Pintelon, Liliane, 2004. "Maintenance concept development: A case study," International Journal of Production Economics, Elsevier, vol. 89(3), pages 395-405, June.
    11. A H Christer, 1999. "Developments in delay time analysis for modelling plant maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1120-1137, November.
    12. Barabadi, Abbas & Barabady, Javad & Markeset, Tore, 2011. "Maintainability analysis considering time-dependent and time-independent covariates," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 210-217.
    13. Bekker, Leonid & Mi, Jie, 2003. "Shape and crossing properties of mean residual life functions," Statistics & Probability Letters, Elsevier, vol. 64(3), pages 225-234, September.
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    Cited by:

    1. Peng, Weiwen & Huang, Hong-Zhong & Li, Yanfeng & Zuo, Ming J. & Xie, Min, 2013. "Life cycle reliability assessment of new products—A Bayesian model updating approach," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 109-119.

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