A Control and Fault Diagnosis Method for Pressure Sensor Based Brake Control System

2005 ◽  
Author(s):  
Manbok Park ◽  
Hyungjin Kang ◽  
Paljoo Yoon ◽  
Inyong Hwang
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Pressure Sensor ◽  
Diagnosis Method

Fault Diagnosis for Suspension Signal of Maglev Vehicle Based on Mathematical Model

2012 ◽  
Vol 190-191 ◽  
pp. 987-992 ◽  
Author(s):  
Ying Pu Cui ◽  
Long Hua She ◽  
Xiao Long Li ◽  
A Ming Hao
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Fault Diagnosis ◽  
System Model ◽  
Maglev Vehicle ◽  
Suspension Control ◽  
Diagnosis Method ◽  
Real Value ◽  
Control System Model

Firstly, build the suspension-control-system model under the condition of elastic guideway, and design the controller. Secondly, design the Kalman forecaster based on model, and diagnose the fault by comparing forecasted value with real value. Finally, verify the effectiveness of this fault diagnosis method for suspension signal by simulation.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

scholarly journals Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

2019 ◽  
Vol 9 (19) ◽  
pp. 4122 ◽  
Author(s):  
Bo Wang ◽  
Hongwei Ke ◽  
Xiaodong Ma ◽  
Bing Yu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Engine Control ◽  
Piston Engine ◽  
Engine Control System ◽  
Diagnosis Method

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

A Sensor Fault Diagnosis Method for Gas Turbine Control System Based on EMD and SVM

2021 ◽  
Author(s):  
Daogang Peng ◽  
Shihao Yun ◽  
Debin Yin ◽  
Bin Shen ◽  
Chao Xu ◽  
...  
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Sensor Fault ◽  
Diagnosis Method
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