PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

Yuxiu       Jiang; Xiaohuan      Zhao

doi:10.2174/1872212113666190911124334

Abstract

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents.

Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition.

Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test.

Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Keywords: Electronic accelerator pedal, detection, PNN, BP, fault diagnosis, effective fault detection.

Graphical Abstract

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DOI https://dx.doi.org/10.2174/1872212113666190911124334	Print ISSN 1872-2121
Publisher Name Bentham Science Publisher	Online ISSN 2212-4047

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