Name
Title | ||
|---|---|---|
Leaks Detection And Characterization In Diesel Air Path Using Levenberg-Marquardt Neural Networks |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IVS.2012.6232308 | 2012 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) |
Keywords | Field | DocType |
mathematical model,fault detection,atmospheric modeling,sensors,fault isolation,neural nets,estimation,neural networks,torque | Automotive engineering,Diesel fuel,Torque,Leak,Fault detection and isolation,Engineering,Diesel engine,Artificial neural network,Levenberg–Marquardt algorithm,Automotive industry | Conference |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mourad Benkaci | 1 | 3 | 2.44 |
| Ghaleb Hoblos | 2 | 14 | 6.40 |
| Karim Ben-Cherif | 3 | 1 | 0.36 |