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Machine Health Indicator Construction Framework for Failure Diagnostics and Prognostics

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Abstract

Condition monitoring (CM) data should undergo through preprocessing to extract health indicators (HIs) for proper system health assessment. Machine health indicators provide vital information about health state of subcomponents(s) or overall system. There are many techniques in the literature used to construct HIs from CM data either for failure diagnostics or prognostics purposes. The majority of proposed HI extraction methods are mostly application specific (e.g. gearbox, shafts, and bearings etc.). This paper provides an overview of the used techniques and proposes an HI extraction, evaluation, and selection framework for monitoring of different applications. The extracted HIs are evaluated through a compatibility test where they can be used in either failure diagnostics or prognostics. An HI selection is carried out by a new hybrid feature goodness ranking metric in feature evaluation. The selected features are then used in fusion to get the representative component HI. Several case study CM data are used to demonstrate the essentiality of the proposed framework in component monitoring.

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Acknowledgments

This research was supported by a grant from ENIT, Production Engineering Laboratory (LGP), funded by ALSTOM. Gearbox condition monitoring data were provided by the ALSTOM. Point machine and Li-ion degradation datasets were taken from the projects (#108 M275 and #113 M093), which were supported by The Scientific and Technological Research Council of Turkey (TUBITAK).

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Authors and Affiliations

  1. Assystem Energy & Infrastructure, Imagine Laboratory, Tour Egée 11, 92400 Courbevoie, France

    Vepa Atamuradov

  2. Production Engineering Laboratory (LGP) INP-ENIT, 47 Avenue d’Azereix, 65000, Tarbes, France

    Kamal Medjaher

  3. Amazon Inc. 11501 Alterra Parkway, TX78758, Austin, USA

    Fatih Camci

  4. FEMTO-ST, UMR CNRS-UFC/ENSMM, 15B Avenue des Montboucons, Besançon, France

    Noureddine Zerhouni

  5. ALSTOM Transport, 93400 Saint-Ouen, France

    Pierre Dersin & Benjamin Lamoureux

Authors
  1. Vepa Atamuradov
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  2. Kamal Medjaher
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Correspondence to Vepa Atamuradov.

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Atamuradov, V., Medjaher, K., Camci, F. et al. Machine Health Indicator Construction Framework for Failure Diagnostics and Prognostics. J Sign Process Syst 92, 591–609 (2020). https://doi.org/10.1007/s11265-019-01491-4

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