Version 1
: Received: 2 July 2024 / Approved: 2 July 2024 / Online: 2 July 2024 (11:44:52 CEST)
How to cite:
Torres, N. N. S.; Lima, J. G.; Maciel, J. N.; Gazziro, M.; Filho, A. C. L.; Salvadori, F.; Ando Junior, O. H. Non-invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review. Preprints2024, 2024070218. https://doi.org/10.20944/preprints202407.0218.v1
Torres, N. N. S.; Lima, J. G.; Maciel, J. N.; Gazziro, M.; Filho, A. C. L.; Salvadori, F.; Ando Junior, O. H. Non-invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review. Preprints 2024, 2024070218. https://doi.org/10.20944/preprints202407.0218.v1
Torres, N. N. S.; Lima, J. G.; Maciel, J. N.; Gazziro, M.; Filho, A. C. L.; Salvadori, F.; Ando Junior, O. H. Non-invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review. Preprints2024, 2024070218. https://doi.org/10.20944/preprints202407.0218.v1
APA Style
Torres, N. N. S., Lima, J. G., Maciel, J. N., Gazziro, M., Filho, A. C. L., Salvadori, F., & Ando Junior, O. H. (2024). Non-invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202407.0218.v1
Chicago/Turabian Style
Torres, N. N. S., Fabiano Salvadori and Oswaldo Hideo Ando Junior. 2024 "Non-invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202407.0218.v1
Abstract
This article provides a detailed analysis of non-invasive techniques for 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 enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
