Neurofuzzy Model Optimized for Integrating Explainability in the Prediction of Engine Performance Losses
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Published
Jun 19, 2026
Marcela Djoukouo
Virginie Tjahe Agnes
Morgin Carlos Feudjio Nguefack
Blaise Eugène Mtopi Fotso
Abstract
The anticipation of automotive failures in general and the prediction of engine performance losses remain challenging for vehicle owners and automotive industry professionals. In this article, we start by analyzing the causes of engine performance loss to identify the significant parameters of this failure mode. These parameters are then identified as inputs for the implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) neurofuzzy models optimized by a Particle Swarm Optimization (PSO) algorithm that takes into account the four previous instants to predict the next instant. The model was used to predict the performance loss characteristic failures of engine overheating, air leakage, engine power loss, air-to-air heat exchanger fouling, and filter clogging. The proposed model is an Explainable solution that better compromises performance and complexity. The performance of the ANFIS-PSO algorithm was evaluated by comparing test data with actual data. Satisfactory results were obtained, with R2 of the order of 0.99 for the test and training data, Root Mean Square Error (RMSE) of the order of 10-14, Standard Deviation of prediction Errors (Error St. D) of the order of 10-15 and Mean Absolute Error of the order of 10-15 for a prediction horizon of 1800s. This is with a Central Processing Unit (CPU) time of 0.002s. It is clear that the ANFIS-PSO model, which considers the four previous time instants, is sufficiently performant to predict the phenomena associated with the loss of engine performance.
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Keywords
Engine performance losses, Neurofuzzy optimized, Characteristic failures, Explainable solution, Performance and Complexity Trade-off.
References
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Banoqitah, E. et al. (2023). Enhancement and prediction of a stepped solar still productivity integrated with paraffin wax enriched with nano-additives. Case Studies in Thermal Engineering, 49, 103215.
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Hosseini, S. et al. (2022). Novel and robust machine learning approach for estimating the fouling factor in heat exchangers. Energy Reports, 8, 8767–8776.
J. Kennedy and R. Eberhart (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks. pp. 1942–1948 vol.4.
J. Pranav.P et al. (2023). Smart Fleet: An Advanced Web-Based Solution for Fleet Management and Security. 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT). pp. 530–535.
Jang, J.-S. et al. (1997). Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]. Automatic Control, IEEE Transactions on, 42, 1482–1484.
Jiang, J. et al. (2024). Feature selection for classification with Spearman’s rank correlation coefficient-based self-information in divergence-based fuzzy rough sets. Expert Systems with Applications, 249, 123633.
Keneni, B. et al. (2019a). Evolving Rule Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles. IEEE Access, PP, 1–1.
Keneni, B. et al. (2019b). Evolving Rule Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles. IEEE Access, PP, 1–1.
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Kontoni, D.-P. N. and Ahmadi, M. (2024). 8 - Practical prediction of ultimate axial strain and peak axial stress of FRP-confined concrete using hybrid ANFIS-PSO models. In Nehdi, M. L. et al. (eds), Artificial Intelligence Applications for Sustainable Construction. Woodhead Publishing, pp. 225–255.
Lazreg, M. and Benamrane, N. (2022). Hybrid system for optimizing the robot mobile navigation using ANFIS and PSO. Robotics and Autonomous Systems, 153, 104114.
Li, X. et al. (2023). Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines. Reliability Engineering & System Safety, 233, 109124.
Liu, Q. et al. (2023). Residual useful life prognosis of equipment based on modified hidden semi-Markov model with a co-evolutional optimization method. Computers & Industrial Engineering, 182, 109433.
Lughofer, E. and Pratama, M. (2023). Evolving multi-user fuzzy classifier system with advanced explainability and interpretability aspects. Information Fusion, 91, 458–476.
M. G. K. Machesa et al. (2019). Evaluation of the Stirling Heat Engine Performance Prediction Using ANN-PSO and ANFIS Models. 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI). pp. 217–222.
M. Querales et al. (2023). Explainability of Machine Learning Models for Hydrological Time Series Forecasting: The Case of Neuro-Fuzzy Approaches. 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). pp. 1–6.
M. S. Kamal et al. (2022). Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning. IEEE Transactions on Instrumentation and Measurement, 71, 1–9.
M. S. Punith et al. (2022). IoT Enabled Smart Fleet Management. 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). pp. 256–260.
Machesa, M. G. K. et al. (2023a). Performance analysis of stirling engine using computational intelligence techniques (ANN & Fuzzy Mamdani Model) and hybrid algorithms (ANN-PSO & ANFIS). Neural Computing and Applications, 35, 2, 1225–1245.
Machesa, M. G. K. et al. (2023b). Performance analysis of stirling engine using computational intelligence techniques (ANN & Fuzzy Mamdani Model) and hybrid algorithms (ANN-PSO & ANFIS). Neural Computing and Applications, 35, 2, 1225–1245.
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Mohanty, S. et al. (2020). Intelligent prediction of engine failure through computational image analysis of wear particle. Engineering Failure Analysis, 116, 104731.
Mottahedi, A. et al. (2018). Overbreak prediction in underground excavations using hybrid ANFIS-PSO model. Tunnelling and Underground Space Technology, 80, 1–9.
Mueller, P. N. et al. (2023). Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement. Engineering Applications of Artificial Intelligence, 126, 106834.
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Technical Papers