OBD of Diesel EGR Using Artificial Neural Networks 2009-01-1427
To detect malfunctions of the EGR system of a passenger car diesel engine, a neural network approach was selected using Self Organizing Maps (SOM). Self Organizing Maps are self-learning technologies that can be used to retrieve typical data patterns in large data sets. This technology is very efficient for identifying if patterns from a new, modified or changed system are similar to already existing patterns. The SOM outputs a measure of similarity to ‘typical system behavior patterns’. As an OBD function, this value is a measure for system anomaly detection.
Performing dynamic tests using standard driving cycles, not only was the occurrence of a malfunction within the EGR system detected by the neural network, the cause of the malfunction could also be identified.
Citation: Fischer, M., Boettcher, J., Kirkham, C., and Georgi, R., "OBD of Diesel EGR Using Artificial Neural Networks," SAE Technical Paper 2009-01-1427, 2009, https://doi.org/10.4271/2009-01-1427. Download Citation
Author(s):
Michael Fischer, Joerg Boettcher, Chris Kirkham, Richard Georgi
Affiliated:
Honda R&D Europe (Deutschland) GmbH, Axeon Technologies Ltd.
Pages: 14
Event:
SAE World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Electronic Engine Controls, 2009-SP-2248
Related Topics:
Neural networks
Diesel / compression ignition engines
Exhaust gas recirculation (EGR)
On-board diagnostics (OBD)
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