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,
Oswaldo Hideo Ando Junior
Affiliations
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
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.
