Tools for predictive maintenance of diesel engines: a systematic bibliographic review

Authors

DOI:

https://doi.org/10.33448/rsd-v9i11.10195

Keywords:

Predictive Maintenance; Diesel engines; Systematic literature review.

Abstract

The fleet maintenance sector represents a large part of the costs in agro-industrial properties and all the innovation and technology used to reduce these costs directly impacts the final price of the product. Given this context, the objective of this article is to identify how the state of knowledge about predictive maintenance tools for diesel engines is configured. To meet the objective, a systematic bibliographic review was used, consisting of three phases: Input, Processing and Output. It was possible to identify an advance in scientific production related to predictive maintenance tools, which reinforces its importance. When analyzing the documents in full, it was possible to categorize the documents by applicability, being: Industrial; Diesel Engines; 2T Diesel Engines; Diesel Engines for Agricultural Tractors; Engines and Mechanical equipment; Bus and Passenger Transport; Mining and Maritime. It was also possible to conclude that the sectors that research and develop the most predictive maintenance tools for engines are industrial and marine. Of the 41 documents analyzed in this research, eight are book chapters, which demonstrates that the analysis of such a documentary format is relevant to the theme addressed here. Likewise, research on predictive maintenance has been gaining importance in recent years, which leads us to believe that it must also move towards the agricultural sector.

Author Biographies

Evelim Larissa Rombi De Aquino , São Paulo State University (UNESP), School of Sciences and Engineering

Master's student of the Graduate Program in Agribusiness and Development (PGAD) at the Faculty of Science and Engineering of the Universidade Estadual Paulista "Júlio de Mesquita Filho" - UNESP Tupã / SP unit.

Mario Mollo Neto, São Paulo State University (UNESP), Faculty of Science and Engineering, Tupã/SP Brazil

Prof. Dr. Mario Mollo Neto, CNPq Scholar - DT-II Process: 313339 / 2019-8 - Productivity in Technological Development and Innovative Extension, Free Lecturer in Digital Circuits at Universidade Estadual Paulista "Júlio de Mesquita Filho" UNESP; (2019). He has a Post Doctorate in Biosystems Engineering in the area of Rural Constructions and Ambience, from the State University of Campinas (2009), a Doctorate in Agricultural Engineering (CAPES Concept 5) in the area of Rural Constructions and Ambience from the State University of Campinas (2007) , Master's in Production Engineering (CAPES Concept 5) from Universidade Paulista UNIP (2004), and a degree in Industrial Engineering from the São Judas Tadeu University (USJT) (1987). He is currently an Associate Professor in the Biosystems Engineering Course at the Faculty of Science and Engineering (FCE) at Universidade Estadual Paulista - UNESP in TUPÃ.

Department of Biosystems Engineering.

Cristiane Hengler Corrêa Bernardo, São Paulo State University (UNESP), Faculty of Science and Engineering, Tupã/SP Brazil

He holds a PhD in Education from UFMS (2010); Master in Media Communication from UNESP (2002); Specialization in Communication and Marketing and Graduation in Social Communication with a Degree in Journalism from the Pontifical Catholic University of Campinas (1990). He is a professor in Business Communication at UNESP (2018). She was Coordinator of the Administration Course and is currently Associate Professor at UNESP - Faculty of Sciences and Engineering. He teaches the disciplines of Business Communication and Oriented Interdisciplinary Work IV and V for the Course on Administration and Construction of Interdisciplinary Knowledge, Research and Communication Methodology, Networks and Culture for the Interdisciplinary Master in Agribusiness and Development (PGAD). Develops research project in the field of Rural Communication, Social and Environmental Responsibility and Education and Work. He is a member of the following research groups: CEPEAGRO and Research in Management and Environmental Education (PGEA). She worked at the Estácio Participações Group as Academic Director of Faculdade Estácio de Sá in Campo Grande and Coordinator of the Journalism Course (from 2004 to 2006) and as Executive Director and General Director of Faculdade Integrada do Recife (2006 to 2008).

Flávio José de Oliveira Morais, São Paulo State University (UNESP), Faculty of Science and Engineering, Tupã/SP Brazil

Bachelor in Computer Engineering with an emphasis on Industrial Automation from the Pontifical Catholic University of Goiás (2009). Master in Electrical Engineering from the State University of Campinas (2011) and Doctorate (2015) held at the Department of Semiconductors, Instruments and Photonics (DSIF) at the Faculty of Electrical and Computer Engineering (FEEC) at the State University of Campinas. He is currently a professor at UNESP where he teaches the disciplines of Microcontrollers, Microprocessed Systems and Electronic Devices and Circuits. He also conducts research as a collaborator in the Department of Semiconductors, Instruments and Photonics - DSIF at UNICAMP in the area of ​​Electronic Instrumentation, Wireless Sensor Networks and Embedded Systems. He has experience in the area of ​​Electrical and Computer Engineering, with an emphasis on Electronic Instrumentation, acting mainly on the following topics: analog and digital electronic circuits, wireless sensor networks, circuits and components for Energy Harvesting and electronic instrumentation for embedded systems.

Paulo Sérgio Barbosa dos Santos, São Paulo State University (UNESP), Faculty of Science and Engineering, Tupã/SP Brazil

Graduated in Mechatronics Engineering from UniSALESIANO de Araçatuba-SP (2010), Master in Mechanical Engineering from Universidade Estadual Paulista-UNESP (2013) as a CNPq fellow (National Council for Scientific and Technological Development), PhD in Mechanical Engineering from UNESP (2017). He works as Assistant Professor in the Biosystems Engineering Course at UNESP, Campus de Tupã - SP and Assistant Editor in the Revista Brasileira de Engenharia de Biosystems (BIOENG).

References

Abdul-Munaim, A. M., Reuter, M., Abdulmunem, O. M., Balzer, J. C., Koch, M. & Watson, D. G. (2016). Using terahertz time-domain spectroscopy to discriminate among water contamination levels in diesel engine oil. Transactions of the ASABE. American Society of Agricultural and Biological Engineers, St. Joseph, Michigan. 59 (3) 795-801. doi.org/10.13031/trans.59.11448.801.

Alvarez, I. & Huet, S. (2008). Automatic diagnosis of engine of agricultural tractors: The BED experiment. Biosystems Engineering, 100 (1) 362-369. doi.org/10.1016/j.biosystemseng.2008.04.003.

Bin, Z., Jun, Y. & Cheng, T. (2008). Study on fault diagnosis system of diesel engine fuel injection system based on BP neural network. International Colloquium on Computing, Communication, Control, and Management, CCCM 2008, (2) 108-112. doi.org/10.1109/CCCM.2008.33.

Biolchini, J. C. A., Mian, P. G., Natali, A. C. C., Conte, T. U. & Travassos, G. H. (2007). Scientific research ontology to support systematic review in software engineering. Advanced Engineering Informatics, 21(2) 133-151. doi.org/10.1016/j.aei.2006.11.006.

Bloch, H. P. & Geitner, F. K. (1999). Chapter 7 - Statistical Approaches in Machinery Problem Solving. In: Practical Machinery Management for Process Plants. Gulf Professional Publishing, 2 (1) 477-522. doi.org/10.1016/S18746942(99)80009-1.

Boullosa-Falces, D., Barrena, J. L. L., Lopez-Arraiza, A., Menendez, J. & Solaetxe, M. A. G. (2017). Monitoring of fuel oil process of marine diesel engine. Applied Thermal Engineering, 127 (1) 517-526. doi.org/10.1016/j.applthermale.2017.08.036

Conforto, E. C., Amaral, D. C. & Silva, S. L. (2011). Roteiro para revisão bibliográfica sistemática: aplicação no desenvolvimento de produtos e gerenciamento de projeto. VIII Congresso Brasileiro de Gestão de Desenvolvimento de Produto. Porto Alegre/RS.

Cruz-Peragón, F., Palomar, J.M., Díaz, F.A. & Jiménez-Espadafor, F.J. (2009). Practical identification of non-linear characteristics of elastomeric couplings in engine assemblies. Mechanical Systems and Signal Processing, 23 (1) 922-930. doi.org/10.1016/j.ymssp.2008.08.007.

Diez-olivan, A., Pagan, J. A., Sanz, R. & Sierra, B. (2017). Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF based score. Neurocomputing, 241(1) 97-107. doi.or/10.1016/j.neucom.02.024.

Engineered Machined Products Inc. (2000). A breakthrough in vehicle cooling system pumps design. World Pumps, 2000 (1) 42-44. doi.org/10.1016/S0262-1762(00)88913-0.

García, M.O., Ordóñez, C.G. & Torregrosa, A.J. (2016). Noise analysis in the design and construction of a semianechoic chamber for the diagnosis of internal combustion engines. Informacion Tecnologica, 27(1) 121-132. doi.org/10.4067/S071807642016000500014.

Geitner, F. K. & Bloch, H. P. (2006). Chapter 4 - Estimating machinery uptime. In: Practical Machinery Management for Process Plants. Gulf Professional Publishing, 5(1) 45-77.

Girdhar, P.& Scheffer, C. (2004). Chapter 1 - Predictive maintenance techniques: Part 1 predictive maintenance basics. In: Practical Machinery Vibration Analysis and Predictive Maintenance. Newnes, 1-10.

Gomes, H.M. & Silva, N.R.S. (2008). Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method. Applied Mathematical Modelling, 32(1) 2216-2232. doi.org/10.1016/j.apm.2007.07.002.

Goodman, D. Sorj, B. & Wilkinson, J. (2008). Da lavoura às biotecnologias: agricultura e indústria no sistema internacional. Rio de Janeiro: Centro Edelstein de Pesquisas Sociais. A apropriação industrial do processo de produção rural. 6-49. ISBN: 978-85-9966-229-8.

Huang, M., Liu, Z. & Tao, Y. (2019). Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simulation Modelling Practice and Theory. doi.org/10.1016/j.simpat.2019.101981.

Isa, M.C., Yusoff, N.H.N., Nain, H., Yati, M. S. D., Muhammad, M.M.& Nor, I. M. (2013). Ferrographic Analysis of Wear Particles of Various Machinery Systems of a Commercial Marine Ship. Procedia Engineering, 68(1) 345-351. doi.org/10.1016/j.proeng.2013.12.190.

Jacto. (2020). Entenda a mecanização da agricultura e conheça 4 vantagens. Sitio eletrônico. Recuperado de https://blog.jacto.com.br/entenda-a-mecanizacao-da-agricultura-e-conheca-4-vantagens/

Kardec, A. & Nascif J. (2009). Manutenção: função estratégica. 3.ed. Rio de Janeiro: Qualitymark: Petrobrás.

Köche, J. C. (2011). Fundamentos de metodologia científica: teoria da ciência e iniciação à pesquisa. Petrópolis, RJ: Vozes.

Kumar, A. & Ghosh, S. K. (2019). Size distribution analysis of wear debris generated in HEMM engine oil for reliability assessment: A statistical approach. Measurement, 131(1) 412-418. doi.org/10.1016/j.measurement.2018.09.012.

Kumar, A., Shankar, R. & Thakur, L. S. (2018). A big data driven sustainable manufacturing framework for condition-based maintenance prediction. Journal of Computational Science, 27(1) 428-439. doi.org/10.1016/j.jocs.2017.06.006.

Lazakis, I., Raptodimos, Y. & Varelas, T. (2018). Predicting ship machinery system condition through analytical reliability tools and artificial neural networks. Ocean Engineering, 152(1) p. 404-415. doi.org/10.1016/j.oceaneng.2017.11.017.

Lazakis, I., Dikis, K., Michala, A. L.& Theotokatos, G. (2016). Advanced Ship Systems Condition Monitoring for Enhanced Inspection, Maintenance and Decision Making in Ship Operations. Transportation Research Procedia, 14(1) 1679-1688. doi.org/10.1016/j.trpro.2016.05.133.

Lazzerini, F. T. & Bonotto, D. M. (2014). O silício em águas subterrâneas do Brasil.

Ciência e Natura, 36(2) 159–168. doi.org/10.5902/2179460X13135.

Lee, J., Kao, H. & Yang, S. (2014). Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment. Procedia CIRP, 16(1), p. 3-8. doi.org/10.1016/j.procir.2014.02.001.

Li, Z., Jiang, Y., Duan, Z. & Peng, Z. (2018). A new swarm intelligence optimized multiclass multi-kernel relevant vector machine: An experimental analysis in failure diagnostics of diesel. Structural Health Monitoring, 17(1) 1503-1519. doi.org/10.1177/1475921717746735.

Ludke, M. & Andre, M. E . D. A. (2013). Pesquisas em educação: uma abordagem qualitativa. São Paulo: E.P.U. F.

Macian, V., Payri, R., Tormos, B. & Montoro, L. (2006). Applying analytical ferrography as a technique to detect failures in Diesel engine fuel injection systems. Wear. 260(1) 562-566. doi.org/10.1016/j.wear.2005.03.019.

Magro, T. V. & Cavichioli, F. A. (2018). Uso de implementos agrícolas: vantagens e desvantagens. SIMTEC. Simpósio de Tecnologia da Fatec de Taquaritinga. 4(1)1-13.

Marion, C. (2014). Mecanização agrícola transforma perfil do gestor. Revista Máquinas Agrícolas & Inovações.

Mobley, R. K. (2001). 43 - Vibration Fundamentals. In: Plant Engineer's Handbook. Butterworth-Heinemann. doi.org/10.1016/B978-075067328-0/50045-8.

Mobley, R. K. (1999). Chapter 2 - Vibration Analysis Applications. In: Vibration

Fundamentals. Newnes, 3-5. doi.org/10.1016/B978-075067150-7/50038-0.

Mobley, R. K. (2002). 9 – Tribology: An Introduction to Predictive Maintenance. In: Plant Engineering, Butterworth-Heinemann, 2. ed. doi.org/10.1016/B978075067531-4/50009-9.

Moore, R. (2007). 13 - Predictive maintenance/ condition monitoring. In: Selecting the Right Manufacturing Improvement Tools. Butterworth-Heinemann. doi.org/10.1016/B978-075067916-9/50014-X.

Nahim, H. M., Younes, R., Shraim, H. & Ouladsine, M. (2016). Modeling with Fault Integration of the Cooling and the Lubricating Systems in Marine Diesel Engine:

Experimental validation. IFAC, 49(1) 570-575. doi.org/10.1016/j.ifacol.2016.08.083.

Nasiri, A., Taheri-Garavand, A., Omid, M. & Carlomagno, G. M. (2019). Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images, Applied Thermal Engineering, 163(1). doi.org/10.1016/j.applthermaleng.2019.114410.

Nixon, S., Weichel, R., Reichard, K. & Kozlowski, J. (2018). A machine learning approach to diesel engine health prognostics using engine controller data. Proceedings of the Annual Conference of the Prognostics and Health Management Society.

Olver, A.V. (2002). Gear lubrication – a review. Journal of Engineering Tribology. 216(1) 255-267. doi.org/10.1243/135065002760364804.

Oliveira; F. C. M., Lago, D. M., Oliveira Filho, M. F. & Medeiros, J. T. N. M. (2018). A new stochastic model for particulate matter and debris emitted by diesel engines. 6th International Conference Integrity-Reliability-Failure Lisbon/Portugal.

Pacheco, E.P. (2000). Seleção e custo operacional de máquinas agrícolas. Rio Branco: Embrapa Acre, (Embrapa Acre. Documentos, 58).

Patocka, F., Schlögl, M., Schneidhofer, C.; Dörr, N., Schneider, M. & Schmid, U. (2019). Piezoelectrically excited MEMS sensor with integrated planar coil for the detection of ferrous particles in liquids. Sensors and Actuators B: Chemical, 299(1) 126957. doi.org/10.1016/j.snb.2019.126957.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J. & Shitsuka, R. (2018). Metodologia da pesquisa científica. Santa Maria, RS: UFSM, NTE.

Raposo, H.; Farinha, J.T.; Fonseca, I.; Ferreira, L.A. (2019). Condition monitoring with prediction based on diesel engine oil analysis: A case study for urban buses. MDPI, v.8, doi.org/10.3390/act8010014.

Raposo, H., Farinha, J. T. & Fonseca, I.; Galar, D. (2019). Predicting condition based on oil analysis – A case study. Tribology International, 135(1) 65-74. doi.org/10.1016/j.triboint.2019.01.041.

Rubio, J. A. P., Vera-García, F. Grau, J. H., Cámara, J. M. & Hernandez, D. A. (2018). Marine diesel engine failure simulator based on thermodynamic model. Applied Thermal Engineering, 144(1) 982-995. doi.org/10.1016/j.applthermaleng.2018.08.096.

Sampaio, R. F. & Mancini, M. C. (2007). Estudos de revisão sistemática: um guia para síntese criteriosa da evidência científica. Revista Brasileira de Fisioterapia, São Carlos, 11(1) 83-89.

Santos, E. A., Soares, H. S., Silva, M. R., Rocha, T. A. F. & Fernandes, T. J. L. (2014). Uso correto dos óleos lubrificantes para máquinas agrícolas. Revista Cultivar Máquinas. Ed. 143.

Senar – Serviço Nacional de Aprendizagem Rural. (2009). Tratores Agrícolas: manutenção de tratores agrícolas. Coleção SENAR 130. Serviço Nacional de Aprendizagem Rural. Brasília: SENAR.

Sidahmed, M. (2001). Diagnostics and condition monitoring, basic concepts.

Encyclopedia of Vibration, Elsevier. doi.org/10.1006/rwvb.2001.0147.

Silveira, G. M. (2001). Os cuidados com o trator. Série Mecanização. Viçosa: Aprenda Fácil.

Simões, A., Viegas, J.M., Farinha, J.T. & Fonseca, I.(2017). The State of the Art of Hidden Markov Models for Predictive Maintenance of Diesel Engines. Quality and Reliability Engineering International, 33(1) 2765-2779. doi.org/10.1002/qre.2130.

Sitnik, L., Pentós, K., Magdziak-Toklowicz, M. & Wrobel, R. (20150. The Laser Doppler Vibrometry in mechatronics diagnostics. Archives of Civil and Mechanical Engineering, 15(1) 962-970. doi.org/10.1016/j.acme.2015.04.001.

Snook, W. A. (1968). Análise de óleos usados de motores. Lubrificação, 54(9) 97-116.

Syan, C. S. & Ramsoobag, G. (2019). Maintenance applications of multi-criteria optimization: A review. Reliability Engineering & System Safety, 190(1). doi.org/10.1016/j.ress.2019.106520.

Taheri-Garavand, A., Ahmadi, H., Omid, M., Mohtasebi, S. S., Mollazade, K., Smith, A. J. R. & Carlomagno, G. M. (2015). An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique. Applied Thermal Engineering, 87(1) 434-443. doi.org/10.1016/j.applthermaleng.2015.05.038.

Vian, C. E. F., Andrade JR, A. M., Baricelo, G. & Silva, R. P. (2013). Origens, evolução e tendências da indústria de máquinas agrícolas. Rev. Econ. Sociol. Rural. 51(4) 719-744. Brasília. doi.org/10.1590/S0103-20032013000400006.

Villanueva, J. A. B., Espadafor, F. J., Cruz-Peragon, F. & Garcia, M. T. (2011). A methodology for cracks identification in large crankshafts. Mechanical Systems and Signal Processing. 25(8) 3168-3185. doi.org/10.1016/j.ymssp.2011.02.018.

Wang, J., Mao, X., Zhu, K., Song, J. & Zhuo, B. (2009). An intelligent diagnostic tool for electronically controlled diesel engine. Mechatronics, 19(1) 859-867. doi.org/10.1016/j.mechatronics.2009.04.009.

Wolak, A. (2018). TBN performance study on a test fleet in real-world driving conditions using present-day engine oils. Measurement, 114(1) 322-331. doi.org/10.1016/j.measurement.2017.09.044.

Downloads

Published

27/11/2020

How to Cite

AQUINO , E. L. R. D. .; MOLLO NETO, M. .; BERNARDO, C. H. C. .; MORAIS, F. J. de O. .; SANTOS, P. S. B. dos . Tools for predictive maintenance of diesel engines: a systematic bibliographic review . Research, Society and Development, [S. l.], v. 9, n. 11, p. e57691110195, 2020. DOI: 10.33448/rsd-v9i11.10195. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/10195. Acesso em: 15 jan. 2025.

Issue

Vol. 9 No. 11

Section

Review Article

License

Copyright (c) 2020 Evelim Larissa Rombi De Aquino ; Mario Mollo Neto; Cristiane Hengler Corrêa Bernardo; Flávio José de Oliveira Morais; Paulo Sérgio Barbosa dos Santos

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.