Sensor fault detection with low computational cost: A proposed neural network-based control scheme

Author(s):  
Konstantinos Michail ◽  
Kyriakos M. Deliparaschos
2016 ◽  
Vol 24 (1) ◽  
pp. 293-301 ◽  
Author(s):  
Konstantinos Michail ◽  
Kyriakos M. Deliparaschos ◽  
Spyros G. Tzafestas ◽  
Argyrios C. Zolotas

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada

2011 ◽  
Vol 467-469 ◽  
pp. 923-927
Author(s):  
Ai She Shui ◽  
Wei Min Chen ◽  
Li Chuan Liu ◽  
Yong Hong Shui

This paper focuses on the problem of detecting sensor faults in feedback control systems with multistage RBF neural network ensemble-based estimators. The sensor fault detection framework is introduced. The modeling process of the estimator is presented. Fault detection is accomplished by evaluating residuals, which are the differences between the actual values of sensor outputs and the estimated values. The particular feature of the fault detection approach is using the data sequences of multi-sensor readings and controller outputs to establish the bank of estimators and fault-sensitive detectors. A detectability study has also been done with the additive type of sensor faults. The effectiveness of the proposed approach is demonstrated by means of three tank system experiment results.


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