Machine Learning Applied to the Classification of Technical Inspection Recommendations Regarding the Trend to Increase Criticality

Resumo


Context: The problem of prioritizing maintenance activities has become a subject of great interest to the industry in a highly competitive scenario where profitability, productivity, and safety are sought in highly automated plant operations. Problem: The operational unit object of this study faces a high demand for maintenance services, indicated by the technical inspection recommendations (TIR) and the risks arising from not meeting them in a timely manner, with the consequent operational and financial losses. Solution: This article proposes a tool to support the prioritization of equipment maintenance activities through a machine learning model that classifies TIRs according to their criticality, thus providing a second opinion for the classification made by the inspector and, therefore, indicating those that have a greater tendency to escalate in criticality. IS Theory: This work is associated with the Theory of the knowledge-based company, assisting in decision-making and efficiency in the application of resources. Method: To construct the Machine Learning model, algorithms and ensembles used in classification problems and natural language processing methods were used, considering that one of the most relevant attributes of the dataset is a free textual field. Summary of Results: After carrying out several experiments, we arrived at the best performance model, with a recall of 87.08% and an accuracy of 95.19%, values ​​understood as promising for the implementation of the tool in production. Contributions and Impact in the IS area: The main contribution was to identify the feasibility of using machine learning models for the classification of technical inspection recommendations regarding the trend to increase criticality.
Palavras-chave: Industrial maintenance, Service prioritization, Machine learning, Classification models, Natural Language Processing

Referências

Instituto Nacional de Metrologia, Qualidade e Tecnologia-INMETRO. Portaria nº537, de 21 de outubro de 2015: Instrução normativa INMETRO para serviços próprios de inspeção de equipamentos - SPIE. Brasília: INMETRO, 2015.

Çınar, Z.M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. and Safaei, B. 2020. Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability. 12, 19 (Oct. 2020), 8211

Lucchetti, G.P. Aplicação de machine learning para predição de manutenção de máquinas em empresa de transporte e logística de combustíveis. Trabalho de conclusão de curso (Engenharia de Produção). Niterói: Escola de Engenharia, Universidade Federal Fluminense. 2020.

Machado, F.P. Predição da hospitalização de pacientes idosos no departamento de emergência: abordagem utilizando aprendizado de máquina. Dissertação (Mestrado profissional em Gestão para a Competitividade). São Paulo: Escola de Administração de Empresas de São Paulo, Fundação Getúlio Vargas. 2019.

Moraes, M. Tomada de decisão na priorização de pacientes em fila de espera cirúrgica baseada em aprendizado de máquina. Dissertação (Mestrado profissional em Gestão para a Competitividade). São Paulo: Escola de Administração de Empresas de São Paulo, Fundação Getúlio Vargas. 2021.

Escovedo, T. e Koshiyama, A.. Introdução a Data Science - Algoritmos de Machine Learning e métodos de análise. Casa do Código, 2020.

Kalinowski, M.; Escovedo, T.; Villamizar, H.; Lopes, H. “Engenharia de Software para Ciência de Dados: Um Guia de Boas Práticas com Ênfase na Construção de Sistemas de Machine Learning em Python. Editora Casa do Código, 2023.
Publicado
29/05/2023
AMARAL, André; ARAÚJO, André; TOMAZELA, Bruno; ESCOVEDO, Tatiana; KALINOWSKI, Marcos. Machine Learning Applied to the Classification of Technical Inspection Recommendations Regarding the Trend to Increase Criticality. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 19. , 2023, Maceió/AL. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 .

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