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

Piero Baraldi, Michele Compare, Enrico Zio, Francesco Cannarile and Zhe Yang

Journal of Risk and Reliability, 2023, vol. 237, issue 5, 958-965

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.

Keywords: Fault detection; multi-component systems; degradation assessment; deep learning; ensembles of models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:237:y:2023:i:5:p:958-965

DOI: 10.1177/1748006X221107191

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