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Fault Detection and Diagnosis in Condition-Based Predictive Maintenance

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International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 712))

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Abstract

The maintenance of tools, equipment and machines is, today more than ever, at the heart of process optimization that will support market developments while maintaining efficiency and an optimal productivity rate. An accurate and timely maintenance plan will reduce unplanned downtimes, improve machine availability, and reduce the risk of non-compliance. Predictive maintenance (PdM), supported by digital technologies, will allow companies to predict future breakdowns and take early action to prevent them, and this will have a major impact on increasing the availability rate of machines and creating a more secure link with production, while reducing unplanned costs. Fault detection and diagnosis (FDD) lies at the core of PdM with the primary focus on finding anomalies in the working equipment at early stages and alerting the manufacturing supervisor to carry out maintenance activity.

The aim of this paper is to highlight fault detection as a component of predictive maintenance and describe the model-based approach of machine fault diagnosis.

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Authors and Affiliations

  1. LTI Laboratory, ENSA of Tangier, Abdelmalek Essadi University, Tangier, Morocco

    Oumaima El Hairech & Abdelouahid Lyhyaoui

Authors
  1. Oumaima El Hairech
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  2. Abdelouahid Lyhyaoui
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Correspondence to Oumaima El Hairech .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Hairech, O.E., Lyhyaoui, A. (2023). Fault Detection and Diagnosis in Condition-Based Predictive Maintenance. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-35251-5_28

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