Leaks detection and characterization in diesel air path using Levenberg-Marquardt neural networks

Fault detection and isolation are one of the most important steps in automotive diagnosis. In this work, a new OBD scheme is proposed dealing with fault detection and localization problem in diesel engine. Especially, the leak detection and characterization problem in diesel air path is studied. The...

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Bibliographic Details
Main Authors: Benkaci, M., Hoblos, G., Ben-Cherif, K.
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric modeling
Estimation
Mathematical model
Neural networks
Sensors
Torque
Training
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Summary:Fault detection and isolation are one of the most important steps in automotive diagnosis. In this work, a new OBD scheme is proposed dealing with fault detection and localization problem in diesel engine. Especially, the leak detection and characterization problem in diesel air path is studied. The proposed solution is based on the neural network trained using Levenberg-Marquardt algorithm in order to model the engine dynamics. This model is used to detect and characterize any leak occurred in intake part of the air path. The model is learned and validated using data generated by xMOD. This tool is used again for test. The effectiveness of proposed approach is illustrated in simulation when the system run on a low speed, a low load and the considered leak affecting the air path is very small.
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2012.6232308