IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9387-d1000789.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9387/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9387/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    2. Becken, Susanne & Stantic, Bela & Chen, Jinyan & Connolly, Rod M., 2022. "Twitter conversations reveal issue salience of aviation in the broader context of climate change," Journal of Air Transport Management, Elsevier, vol. 98(C).
    3. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    4. Rockstuhl, Sebastian & Wenninger, Simon & Wiethe, Christian & Ahlrichs, Jakob, 2022. "The influence of risk perception on energy efficiency investments: Evidence from a German survey," Energy Policy, Elsevier, vol. 167(C).
    5. Tong Koecklin, Manuel & Fitiwi, Desta & de Carolis, Joseph F. & Curtis, John, 2020. "Renewable electricity generation and transmission network developments in light of public opposition: Insights from Ireland," Papers WP653, Economic and Social Research Institute (ESRI).
    6. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    7. Michele Cincera, 2005. "Firms' productivity growth and R&D spillovers: An analysis of alternative technological proximity measures," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(8), pages 657-682.
    8. Horstmann, Felix, 2017. "Measuring the shopper's attitude toward the point of sale display: Scale development and validation," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 112-123.
    9. Junyong Jang & Yongbin Cho & Juntae Park, 2024. "Bus Route Sketching: A Multimetric Analysis from the User’s and Operator’s Perspectives," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    10. Elizaveta Zinovyeva & Raphael C. G. Reule & Wolfgang Karl Hardle, 2021. "Understanding Smart Contracts: Hype or Hope?," Papers 2103.08447, arXiv.org.
    11. Zhao, Yingrui & Hu, Songhua & Zhang, Ming, 2024. "Evaluating equitable Transit-Oriented development (TOD) via the Node-Place-People model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    12. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    13. Chester Harris, 1955. "Characteristics of two measures of profile similarity," Psychometrika, Springer;The Psychometric Society, vol. 20(4), pages 289-297, December.
    14. Brian C Wesolowski & Alex Hofmann, 2016. "There’s More to Groove than Bass in Electronic Dance Music: Why Some People Won’t Dance to Techno," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    15. Quang Bao Le & Boubaker Dhehibi, 2019. "A Typology-Based Approach for Assessing Qualities and Determinants of Adoption of Sustainable Water Use Technologies in Coping with Context Diversity: The Case of Mechanized Raised-Bed Technology in E," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    16. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A new estimation algorithm for more robust prediction," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    17. Arévalo, Franklim & Barucca, Paolo & Téllez-León, Isela-Elizabeth & Rodríguez, William & Gage, Gerardo & Morales, Raúl, 2022. "Identifying clusters of anomalous payments in the salvadorian payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(1).
    18. Shahzad, Murtuza & Alhoori, Hamed & Freedman, Reva & Rahman, Shaikh Abdul, 2022. "Quantifying the online long-term interest in research," Journal of Informetrics, Elsevier, vol. 16(2).
    19. Ermal Shpuza, 2023. "The shape and size of urban blocks," Environment and Planning B, , vol. 50(1), pages 24-43, January.
    20. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9387-:d:1000789. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.