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.
Real-time fault detection and process control based on multi-channel sensor data fusion
