An unsupervised approach for health index building and for similarity-based remaining useful life estimation

Autor: Sébastien Schwartz, Juan José Montero Jiménez, Rob Vingerhoeds, Michel Salaün
Přispěvatelé: Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole nationale supérieure des Mines d'Albi-Carmaux - IMT Mines Albi (FRANCE), Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Département d'Ingénierie des Systèmes Complexes (DISC), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Computers in Industry
Computers in Industry, 2022, 141, pp.103716. ⟨10.1016/j.compind.2022.103716⟩
ISSN: 0166-3615
DOI: 10.1016/j.compind.2022.103716⟩
Popis: International audience; Predictive maintenance techniques attempt to trigger a maintenance intervention at the right moment by estimating the life expectation. Predictive maintenance is increasingly implemented by automated approaches able to perform diagnostics and prognostics. The main part of recent research in these approaches is focused in machinelearning structures whose reasoning is implicit and cannot be easily explained. This poses a problem for theirimplementation in highly constrained area such as aeronautics. To overcome this constraint, explicit reasoningapproaches such as the Similarity-Based Model (SBM) can be implemented. The SBM has been widely used for fault diagnostics and the remaining useful life (RUL) estimation, but the development of SBM includes tasks that often rely on high skilled experts. For instance, data reduction techniques required for SBM are often performed by experts judgment whose outcomes are not always consistent. The produced features from these techniques are used to build the Health Index that can be used to create the degradation trends that serve as a reference for the SBM. To overcome these difficulties, an automatic and unsupervised approach based on the Kernel Principal Component Analysis is proposed to enhance the Health Index creation. It preserves as much of the sensor information as possible improving the similarity-based RUL estimation. Additionally, when estimating the RUL of a system, the most similar degradation trends stored in the SBM library are used to compute individual RULs, the final RUL is obtained by a fusion rule technique that combines all these individual RULs into a consolidated value. For the fusion rule techniques, a self-adaptive method that does not rely on human expertize is proposed. This fusion rule can benefit of the accumulated knowledge over the SBM operation. This unsupervised approach to develop a SBM is validated with promising results against an equivalent and supervised algorithm that came out best in the 2008 prognostic health management challenge.
Databáze: OpenAIRE
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