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Automated Machine Health Monitoring at an Expert Level

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Abstract

Machine health condition monitoring is evidently a crucial challenge nowadays. Unscheduled breakdowns increase operating costs due to repairs and production losses. Scheduled maintenance implies taking the risk of replacing fully operational components. Human expertise is a solution for an outstanding expertise but at a high cost and for a limited quantity of data only, the analysis being time-consuming. Industry 4.0 and digital factory offer many alternatives to human monitoring. Time, cost and skills are the real stakes. The key point is how to automate each part of the process knowing that each one is valuable. Leaving aside scheduled maintenance, this paper copes with condition-based preventive maintenance and focuses on one fundamental step: the signal processing. After a brief overview of this specific area in which numerous technologies already exist, this paper argues for an automated signal processing at an expert level. The objective is to monitor a system over days, weeks, or years with as great accuracy as a human expert, and even better in regard to data investigation and analysis efficiency. After a data validation step most often ignored, any multimodal signal (vibration, current, acoustic, …) is processed over its entire frequency band in view of identifying all harmonic families and their sidebands. Sophisticated processing such as filtering and demodulation creates relevant features describing the fine complex structures of each spectrum. A time–frequency feature tracking constructs trends over time to not only detect a failure but also to characterize and localize it. Such an automated expert-level processing is a way to raise alarms with a reduced false alarm probability.

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Acknowledgments

We thank Amgad Mohamed for the proofreading of the English language.

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Authors and Affiliations

  1. Univ. Grenoble Alpes, CNRS, Grenoble-INP, GIPSA-lab, Saint-Martin-d’Hères, France

    Nadine Martin & Xavier Laval

  2. Univ. De Toulouse, IRIT, Toulouse, France

    Corinne Mailhes

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  1. Nadine Martin
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  2. Corinne Mailhes
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  3. Xavier Laval
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Correspondence to Nadine Martin.

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Martin, N., Mailhes, C. & Laval, X. Automated Machine Health Monitoring at an Expert Level. Acoust Aust 49, 185–197 (2021). https://doi.org/10.1007/s40857-021-00227-4

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