A Neural Network Technique for Verification of Dynamometer Parasitic Losses

961047

02/01/1996

Event
International Congress & Exposition
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/961047
Pages
9
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.
Additional Details
Publisher
Published
Feb 1, 1996
Product Code
961047
Content Type
Technical Paper
Language
English