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A Novel Generic Diagnosis Algorithm in the Time Domain Representation

Author

Listed:
  • Etienne Dijoux

    (ENERGY Lab—LE2P, University La Reunion, 97415 Saint-Denis, France
    Vehicle and Hydrogen Innovation, Crea + Parts Company, 97438 Sainte-Marie, France)

  • Cédric Damour

    (ENERGY Lab—LE2P, University La Reunion, 97415 Saint-Denis, France)

  • Michel Benne

    (ENERGY Lab—LE2P, University La Reunion, 97415 Saint-Denis, France)

  • Alexandre Aubier

    (Vehicle and Hydrogen Innovation, Crea + Parts Company, 97438 Sainte-Marie, France)

Abstract

The health monitoring of a system remains a major issue for its lifetime preservation. In this paper, a novel fault diagnosis algorithm is proposed. The proposed diagnosis approach is based on a unique variable measurement in the time domain and manages to extract the system behavior evolution. The developed tool aims to be generic to several physical systems with low or high dynamic behavior. The algorithm is depicted in the present paper and two different applications are considered. The performance of the novel proposed approach is experimentally evaluated on a fan considering two different faulty conditions and on a proton exchange membrane fuel cell. The experimental results demonstrated the high efficiency of the proposed diagnosis tool. Indeed, the algorithm can discriminate the two faulty operation modes of the fan from a normal condition and also manages to identify the current system state of health. Regarding the fuel cell state of health, only two conditions are tested and the algorithm is able to detect the fault occurrence from a normal operating mode. Moreover, the very low computational cost of the proposed diagnosis tool makes it especially suitable to be implemented on a microcontroller.

Suggested Citation

  • Etienne Dijoux & Cédric Damour & Michel Benne & Alexandre Aubier, 2022. "A Novel Generic Diagnosis Algorithm in the Time Domain Representation," Energies, MDPI, vol. 16(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:108-:d:1011193
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    References listed on IDEAS

    as
    1. Velmurugan K & Saravanasankar S & Venkumar P & Sudhakarapandian R & Gianpaolo Di Bona & Dimitris Mourtzis, 2022. "Availability Analysis of the Critical Production System in SMEs Using the Markov Decision Model," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-16, September.
    2. Farshad BahooToroody & Saeed Khalaj & Leonardo Leoni & Filippo De Carlo & Gianpaolo Di Bona & Antonio Forcina, 2021. "Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
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