Non-Invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review

Norah Nadia Sánchez Torres, Jorge Gomes Lima, Joylan Nunes Maciel, Mario Gazziro, Abel Cavalcante Lima Filho, Cicero Rocha Souto, Fabiano Salvadori () and Oswaldo Hideo Ando Junior ()
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Norah Nadia Sánchez Torres: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Av. Tancredo Neves, 3147, Foz do Iguaçu 85867-000, PR, Brazil
Jorge Gomes Lima: Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), Jardim Universitário, s/n, João Pessoa 58051-900, PB, Brazil
Joylan Nunes Maciel: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Av. Tancredo Neves, 3147, Foz do Iguaçu 85867-000, PR, Brazil
Mario Gazziro: Information Engineering Group, Department of Engineering and Social Sciences (CECS), Federal University of ABC (UFABC), Av. dos Estados, 5001, Santo André 09210-580, SP, Brazil
Abel Cavalcante Lima Filho: Department of Mechanical Engineering (DEME), Technology Center (CT), Federal University of Paraiba (UFPB), Jardim Universitário, s/n, João Pessoa 58051-900, PB, Brazil
Cicero Rocha Souto: Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), Jardim Universitário, s/n, João Pessoa 58051-900, PB, Brazil
Fabiano Salvadori: Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), Jardim Universitário, s/n, João Pessoa 58051-900, PB, Brazil
Oswaldo Hideo Ando Junior: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Av. Tancredo Neves, 3147, Foz do Iguaçu 85867-000, PR, Brazil

Energies, 2024, vol. 17, issue 23, 1-20

Abstract: This article provides a detailed analysis of non-invasive techniques for the prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state of the art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to improve fault diagnosis, in addition to highlighting the crucial role of non-invasive techniques such as the digital twin in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop the integration of AI algorithms with digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

Keywords: Proknow-C; Methodi Ordinatio; non-invasive diagnostics; predictive technologies; predictive maintenance; real-time monitoring; engine engineering; fault analysis (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: 2024
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