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Using an Explainable Machine Learning Approach to Minimize Opportunistic Maintenance Interventions

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection (PAAMS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1678))

Abstract

The industry 4.0 paradigm, with a wide range of sensors, IoT and big data technologies, has facilitated the assessment of faults in complex mechanical systems. In this paper, a fault diagnosis strategy is presented for opportunistic condition-based maintenance decisions of a single failure mode. Focusing on the challenges of the fault identification task, the proposed method was assessed by conducting a case-study using real-world data. To detect symptoms of screen pack degradation in the company’s coextrusion process, the devised strategy was based on an anomaly approach and a technique for explainable artificial intelligence (XAI). Experimental results for two consecutive production runs of an extruder show that the proposed method effectively identifies clustered anomalies as symptoms of a clogged screen pack.

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Author information

Authors and Affiliations

  1. GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Afonso Lourenço, Marta Fernandes, Alda Canito, Ana Almeida & Goreti Marreiros

Authors
  1. Afonso Lourenço
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  2. Marta Fernandes
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  3. Alda Canito
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  4. Ana Almeida
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  5. Goreti Marreiros
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Corresponding author

Correspondence to Marta Fernandes .

Editor information

Editors and Affiliations

  1. University of Salamanca, Salamanca, Spain

    Alfonso González-Briones

  2. Institute of Engineering - Polytechnic of Porto, Porto, Portugal

    Ana Almeida

  3. Universidad Rey Juan Carlos, Madrid, Spain

    Alberto Fernandez

  4. German International University, Cairo, Egypt

    Alia El Bolock

  5. University of Minho, Braga, Portugal

    Dalila Durães

  6. Universitat Politècnica de València, Valencia, Spain

    Jaume Jordán

  7. National Laboratory of Energy and Geology, Amadora, Portugal

    Fernando Lopes

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Lourenço, A., Fernandes, M., Canito, A., Almeida, A., Marreiros, G. (2022). Using an Explainable Machine Learning Approach to Minimize Opportunistic Maintenance Interventions. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Communications in Computer and Information Science, vol 1678. Springer, Cham. https://doi.org/10.1007/978-3-031-18697-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-18697-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18696-7

  • Online ISBN: 978-3-031-18697-4

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