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

被引：0

作者：

Torres, Norah Nadia Sanchez ^{[1
]}

Lima, Jorge Gomes ^{[2
]}

Maciel, Joylan Nunes ^{[1
]}

Gazziro, Mario ^{[3
]}

Filho, Abel Cavalcante Lima ^{[4
]}

Souto, Cicero Rocha ^{[2
]}

Salvadori, Fabiano ^{[2
]}

Ando Junior, Oswaldo Hideo ^{[1
,2
,5
,6
]}

机构：

[1] Fed Univ Latin Amer Integrat UNILA, Interdisciplinary Postgrad Program Energy & Sustai, Ave Tancredo Neves 3147, BR-85867000 Foz Do Iguacu, PR, Brazil

[2] Fed Univ Paraiba UFPB, Ctr Alternat & Renewable Res CEAR, Smart Grid Lab LabREI, Jardim Univ S-N, BR-58051900 Joao Pessoa, PB, Brazil

[3] Fed Univ ABC UFABC, Dept Engn & Social Sci CECS, Informat Engn Grp, Ave Estados 5001, BR-09210580 Santo Andre, SP, Brazil

[4] Fed Univ Paraiba UFPB, Dept Mech Engn DEME, Technol Ctr CT, Jardim Univ S-N, BR-58051900 Joao Pessoa, PB, Brazil

[5] Fed Rural Univ Pernambuco UFRPE, Acad Unit Cabo De Santo Agostinho UACSA, Res Grp Energy & Energy Sustainabil GPEnSE, Rua Cto & Sessenta & Tres 300, BR-54518430 Cabo De Santo Agostinho, PE, Brazil

[6] Fed Rural Univ Pernambuco UFRPE, Program Energy Syst Engn PPGESE, Acad Unit Cabo De Santo Agostinho UACSA, Rua Cento & Sessenta & Tres 300, BR-54518430 Cabo De Santo Agostinho, PE, Brazil

来源：

ENERGIES | 2024年 / 17卷 / 23期

关键词：

Proknow-C; Methodi Ordinatio; non-invasive diagnostics; predictive technologies; predictive maintenance; real-time monitoring; engine engineering; fault analysis; TEMPERATURE; VIBRATION; DIAGNOSIS; EXHAUST;

D O I：

10.3390/en17236164

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：20

共 67 条

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