A Neural Network Technique for Verification of Dynamometer Parasitic Losses 961047
An on line method for verification of chassis dynamometer operation uses a neural network. During the testing of a vehicle, it is assumed that after a warm up period the parasitic losses remain stable. There is normally no provision for verification of correct dynamometer operation while the test is running. This technique will detect if a component wears or fails during the testing of a vehicle and thus avoid testing under erroneous conditions. A Learning Vector Quantization (LVQ) neural network is trained to recognise poor dynamometer operation in order to signal a fault condition to the operator.
Citation: Davis, A. and Quigley, C., "A Neural Network Technique for Verification of Dynamometer Parasitic Losses," SAE Technical Paper 961047, 1996, https://doi.org/10.4271/961047. Download Citation
Author(s):
Alan G. W. Davis, Chris Quigley
Affiliated:
University of Warwick
Pages: 9
Event:
International Congress & Exposition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Trends in Testing and Instrumentation-SP-1130
Related Topics:
Neural networks
Test equipment and instrumentation
Failure analysis
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