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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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    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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