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A modified approximate method for analysis of degradation data

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  • Thiago Rezende dos Santos
  • Enrico A. Colosimo

Abstract

Estimation of the lifetime distribution of industrial components and systems yields very important information for manufacturers and consumers. However, obtaining reliability data is time consuming and costly. In this context, degradation tests are a useful alternative approach to lifetime and accelerated life tests in reliability studies. The approximate method is one of the most used techniques for degradation data analysis. It is very simple to understand and easy to implement numerically in any statistical software package. This paper uses time series techniques in order to propose a modified approximate method (MAM). The MAM improves the standard one in two aspects: (1) it uses previous observations in the degradation path as a Markov process for future prediction and (2) it is not necessary to specify a parametric form for the degradation path. Characteristics of interest such as mean or median time to failure and percentiles, among others, are obtained by using the modified method. A simulation study is performed in order to show the improved properties of the modified method over the standard one. Both methods are also used to estimate the failure time distribution of the fatigue-crack-growth data set.

Suggested Citation

  • Thiago Rezende dos Santos & Enrico A. Colosimo, 2015. "A modified approximate method for analysis of degradation data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1322-1331, June.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1322-1331
    DOI: 10.1080/02664763.2014.999651
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Julio C. Ferreira & Marta A. Freitas & Enrico A. Colosimo, 2012. "Degradation data analysis for samples under unequal operating conditions: a case study on train wheels," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2721-2739, August.
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