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Product failure pattern analysis from warranty data using association rule and Weibull regression analysis: A case study

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  • Jeon, Jeasu
  • Sohn, So Young

Abstract

The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm “D†provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.

Suggested Citation

  • Jeon, Jeasu & Sohn, So Young, 2015. "Product failure pattern analysis from warranty data using association rule and Weibull regression analysis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 176-183.
    • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:176-183
    DOI: 10.1016/j.ress.2014.08.015
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    References listed on IDEAS

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    1. Wu, Shaomin, 2013. "A review on coarse warranty data and analysis," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 1-11.
    2. Sohn, So Young & Chang, In Sang & Moon, Tae Hee, 2007. "Random effects Weibull regression model for occupational lifetime," European Journal of Operational Research, Elsevier, vol. 179(1), pages 124-131, May.
    3. Murthy, D. N. P. & Blischke, W. R., 1992. "Product warranty management -- III: A review of mathematical models," European Journal of Operational Research, Elsevier, vol. 63(1), pages 1-34, November.
    4. Sohn, So Young & Yoon, Kyung Bok & Chang, In Sang, 2006. "Random effects model for the reliability management of modules of a fighter aircraft," Reliability Engineering and System Safety, Elsevier, vol. 91(4), pages 433-437.
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    Cited by:

    1. Çakır, Erkan & Fışkın, Remzi & Sevgili, Coşkan, 2021. "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Zhang, Tian & Homri, Lazhar & Dantan, Jean-Yves & Siadat, Ali, 2023. "Models for reliability assessment of reconfigurable manufacturing system regarding configuration orders," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Gupta, Sanjib Kumar & Bhattacharya, Debasis, 2022. "Non-parametric estimation of bivariate reliability from incomplete two-dimensional warranty data," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Lin, Kunsong & Chen, Yunxia, 2021. "Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Starling, James K. & Mastrangelo, Christina & Choe, Youngjun, 2021. "Improving Weibull distribution estimation for generalized Type I censored data using modified SMOTE," Reliability Engineering and System Safety, Elsevier, vol. 211(C).

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