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

João Antunes Rodrigues (), Alexandre Martins, Mateus Mendes (), José Torres Farinha, Ricardo J. G. Mateus and Antonio J. Marques Cardoso
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João Antunes Rodrigues: CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
Alexandre Martins: CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
Mateus Mendes: Polytechnic of Coimbra— ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
José Torres Farinha: Polytechnic of Coimbra— ISEC, Quinta da Nora, 3030-199 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

Energies, 2022, vol. 15, issue 24, 1-17

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.

Keywords: maintenance; neural networks; k-means; MLPClassifer; unsupervised learning; supervised learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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