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Testing statistical significance of trends in learning, ageing and safety indicators

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  • Viertävä, Janne
  • Vaurio, Jussi K.

Abstract

A relatively new subject for probabilistic safety methodology is statistical analysis of trends in observed failures and other safety indicators reflecting ageing or learning in operational and maintenance experience at industrial facilities. Random variations of the indicators can mask real changes or cause false alarms. Methodology is proposed for testing statistical significance of apparent trends in safety indicators. Improved methods are developed for detecting both monotonic and non-monotonic trends, some demonstrated by simulation studies and real examples to be more powerful than those known so far. An effective way to use standard trend tests with transformed data for testing exponentiality of data is also demonstrated and found superior to a well-known Lilliefors’ goodness-of-fit test.

Suggested Citation

  • Viertävä, Janne & Vaurio, Jussi K., 2009. "Testing statistical significance of trends in learning, ageing and safety indicators," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1128-1132.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:6:p:1128-1132
    DOI: 10.1016/j.ress.2008.11.011
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    References listed on IDEAS

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    1. Vaurio, Jussi K. & Jänkälä, Kalle E., 2006. "Evaluation and comparison of estimation methods for failure rates and probabilities," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 209-221.
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    Cited by:

    1. Rajkumar Bhimgonda Patil & Basavraj S Kothavale & Laxman Yadu Waghmode, 2019. "Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data," Journal of Risk and Reliability, , vol. 233(2), pages 105-117, April.
    2. Hamzeh Soltanali & A.H.S Garmabaki & Adithya Thaduri & Aditya Parida & Uday Kumar & Abbas Rohani, 2019. "Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing," Journal of Risk and Reliability, , vol. 233(4), pages 682-697, August.
    3. Syamsundar, A. & Naikan, V.N.A. & Wu, Shaomin, 2021. "Extended Arithmetic Reduction of Age Models for the Failure Process of a Repairable System," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Garmabaki, A.H.S. & Ahmadi, Alireza & Block, Jan & Pham, Hoang & Kumar, Uday, 2016. "A reliability decision framework for multiple repairable units," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 78-88.
    5. Regattieri, A. & Manzini, R. & Battini, D., 2010. "Estimating reliability characteristics in the presence of censored data: A case study in a light commercial vehicle manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1093-1102.
    6. Gámiz, Maria Luz & Nozal-Cañadas, Rafael & Raya-Miranda, Rocío, 2020. "TTT-SiZer: A graphic tool for aging trends recognition," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. 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.
    8. Zhi-Ming Wang & Xia Yu, 2013. "Log-linear process modeling for repairable systems with time trends and its applications in reliability assessment of numerically controlled machine tools," Journal of Risk and Reliability, , vol. 227(1), pages 55-65, February.

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