A Neural Network Technique for Verification of Dynamometer Parasitic Losses

Paper #:
  • 961047

Published:
  • 1996-02-01
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.
Pages:
9
Abstract:
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.
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