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Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption

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Intelligent Computing Systems (ISICS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1569))

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Abstract

The availability of combustion engines used in generating sets is essential for the continuity of electrical service in industrial and service processes. There are different diagnostic methods that use artificial intelligence techniques to detect flaws in combustion engines such as ANN, SVM, etc. Although these methods have good results, they are so complicated that they are difficult to implement in practice. Another drawback is that they find restrictions in the detection of multiple faults and in providing a diagnosis that serves as the basis for improving important aspects such as maintenance management and fuel efficiency. This work presents a method of diagnosing faults in the MTU-16V-S4000-G81 internal combustion engines using fuzzy logic tools. The proposal is based on historical data of the process and allows the detection of the incipient deviation of the main parameters that alter the fuel consumption index. It guarantees 96.9% multiple fault detection and offers a fuzzy method that suggests maintenance actions to restore specific fuel consumption to normal immediately, facilitating efficient maintenance management.

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Acknowledgements

The authors appreciate the collaboration of the members of the Research Project: Advanced Automation for the Elaboration and Refinement of Steels (AA-ELACERO) - Code: P211LH021-023 financed by the Stainless Steel Company, ACINOX, Las Tunas in Cuba. We thank the specialists of the Electric and Automatic Group at ACINOX. We are very grateful to the Department of Operations (GEYSEL), for guaranteeing the necessary resources to carry out this research.

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Authors and Affiliations

  1. Department of Operations, Generating Sets and Electric Services, 85100, Bayamo, Granma, Cuba

    J. C. Fernández

  2. Faculty of Electromechanics, Department of Electrical Engineering, University of Camagüey, 70100, Camagüey, Cuba

    L. B. Corrales

  3. Faculty of Electrical Engineering, Department of Automatic Control Engineering, University of Oriente, 90500, Santiago of Cuba, Cuba

    I. F. Benítez

  4. Faculty of Engineering, Department of Engineering, University of the Costa, 080002, Barranquilla, Colombia

    J. R. Núñez

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  1. J. C. Fernández
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  2. L. B. Corrales
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  3. I. F. Benítez
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  4. J. R. Núñez
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Corresponding author

Correspondence to J. C. Fernández .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Yucatán, Merida, Mexico

    Carlos Brito-Loeza

  2. Universidad Autónoma de Yucatán, Merida, Mexico

    Anabel Martin-Gonzalez

  3. Universidad de Chile, Santiago, Chile

    Victor Castañeda-Zeman

  4. Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Asad Safi

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Fernández, J.C., Corrales, L.B., Benítez, I.F., Núñez, J.R. (2022). Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption. In: Brito-Loeza, C., Martin-Gonzalez, A., Castañeda-Zeman, V., Safi, A. (eds) Intelligent Computing Systems. ISICS 2022. Communications in Computer and Information Science, vol 1569. Springer, Cham. https://doi.org/10.1007/978-3-030-98457-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-98457-1_2

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