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The Aramis Data Challenge to prognostics and health management methods for application in evolving environments

Author

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  • Piero Baraldi
  • Michele Compare
  • Enrico Zio
  • Francesco Cannarile
  • Zhe Yang

Abstract

A recurrent difficulty for the effective application of Prognostics and Health Management (PHM) methods is related to the “evolving environments†in which industrial components typically operate. Several factors render the operational environments evolving, including deterioration of components, effects of maintenance activities and changes in working conditions. The issue of evolving environments is even more complicated for multi-component systems, where the degradation of one component can affect the degradation processes of other components, thus modifying their lifetime distributions and the statistical properties of the monitored signals. In an effort to convey research toward practical PHM solutions capable of dealing with evolving environments, the “Aramis challenge on degradation state assessment in evolving environments,†has been launched for the ESREL2020-PSAM15 conference. This work describes the Aramis Data Challenge and associated public dataset, illustrates the methods proposed for its solution and the related results obtained. For the evaluation of the goodness of the fault detection methods, an original metric is introduced, which is a variant of the timeliness metric that has been used in the PHM08 data challenge.

Suggested Citation

  • Piero Baraldi & Michele Compare & Enrico Zio & Francesco Cannarile & Zhe Yang, 2023. "The Aramis Data Challenge to prognostics and health management methods for application in evolving environments," Journal of Risk and Reliability, , vol. 237(5), pages 958-965, October.
    • Handle: RePEc:sae:risrel:v:237:y:2023:i:5:p:958-965
    DOI: 10.1177/1748006X221107191
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    1. Linkan Bian & Nagi Gebraeel, 2014. "Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions," IISE Transactions, Taylor & Francis Journals, vol. 46(5), pages 470-482.
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    3. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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