Fault Detection for Aircraft Fuel System with Neural Network

2020 ◽  
Author(s):  
Nithya Subramanian ◽  
Hongmei He ◽  
Ian Jennions
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fuel System ◽  
Aircraft Fuel

Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis

2017 ◽  
Vol 238 ◽  
pp. 13-23 ◽  
Author(s):  
Zehai Gao ◽  
Cunbao Ma ◽  
Dong Song ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuel System ◽  
Aircraft Fuel

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

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

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.

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection

scholarly journals Utilization of Low-Potential Energy Through to the Use in Aircraft Fuel System

2021 ◽  
Vol 1102 (1) ◽  
pp. 012007
Author(s):  
Renat R. Badykov ◽  
Roman A. Panshin ◽  
Olga V. Tremkina ◽  
Anastasia A. Prokofieva
Keyword(s):  
Potential Energy ◽  
Fuel System ◽  
Aircraft Fuel
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