Application of neural and bayesian networks in diesel engines under the flaw detection method

The identification of premature faults in Internal Combustion Engines has become determinant to guarantee suitable operation. Therefore, this study focuses on the implementation of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural networks and Bayesian network...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-07, Vol.1981 (1), p.12003
Main Authors: Prada Botia, G C, Pabón León, J A, Orjuela Abril, M S
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
Bayesian analysis
Diesel engines
Engines
Fault detection
Flaw detection
Internal combustion engines
Methodology
Network reliability
Reliability aspects
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Summary:The identification of premature faults in Internal Combustion Engines has become determinant to guarantee suitable operation. Therefore, this study focuses on the implementation of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural networks and Bayesian networks. Results indicated that the proposed methodology serves as a robust tool to identify different fault conditions in a wide operational spectrum with an reliability of nearly 73%. Moreover, the Backpropagation network diagnostic methodology presented an reliability of 18%, which is 3% higher than Bayesian networks. Overall, the implemented methodology counterbalanced interference conditions and noise signals while providing versatility to operate for different types of engines. In conclusion, this study can be extrapolated to different fields of physics to assist in identifying flaws in experimental test benches.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1981/1/012003