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Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning

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  • João Antunes Rodrigues

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
    EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lusófona, Campo Grande 376, 1749-024 Lisboa, Portugal)

  • Alexandre Martins

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
    EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lusófona, Campo Grande 376, 1749-024 Lisboa, Portugal)

  • Mateus Mendes

    (Polytechnic of Coimbra— ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
    Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-194 Coimbra, Portugal)

  • José Torres Farinha

    (Polytechnic of Coimbra— ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
    Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, 3030-290 Coimbra, Portugal)

  • Ricardo J. G. Mateus

    (EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lusófona, Campo Grande 376, 1749-024 Lisboa, Portugal)

  • Antonio J. Marques Cardoso

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal)

Abstract

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification.

Suggested Citation

  • João Antunes Rodrigues & Alexandre Martins & Mateus Mendes & José Torres Farinha & Ricardo J. G. Mateus & Antonio J. Marques Cardoso, 2022. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9387-:d:1000789
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    References listed on IDEAS

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    1. José Edmundo de Almeida Pais & Hugo D. N. Raposo & José Torres Farinha & Antonio J. Marques Cardoso & Pedro Alexandre Marques, 2021. "Optimizing the Life Cycle of Physical Assets through an Integrated Life Cycle Assessment Method," Energies, MDPI, vol. 14(19), pages 1-24, September.
    2. João Antunes Rodrigues & José Torres Farinha & Mateus Mendes & Ricardo J. G. Mateus & António J. Marques Cardoso, 2022. "Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition," Energies, MDPI, vol. 15(17), pages 1-16, August.
    3. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, December.
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    1. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.

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