Attitude sensor fault diagnosis based on Kalman filter of discrete-time descriptor system

2012 ◽  
Vol 23 (6) ◽  
pp. 914-920 ◽  
Author(s):  
Zhenhua Wang ◽  
Yi Shen ◽  
Xiaolei Zhang
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Discrete Time ◽  
Descriptor System ◽  
Sensor Fault ◽  
Attitude Sensor

Sensor fault diagnosis for fast steering mirror system based on Kalman filter

2015 ◽  
Author(s):  
Hongju Wang ◽  
Qiliang Bao ◽  
Haifeng Yang ◽  
Sunjie Tao
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Mirror System ◽  
Sensor Fault

scholarly journals Simulation of Sensor Fault Diagnosis for Wind Turbine Generators DFIG and PMSM Using Kalman Filter

2014 ◽  
Vol 54 ◽  
pp. 494-505 ◽  
Author(s):  
R. Saravanakumar ◽  
M. Manimozhi ◽  
D.P. Kothari ◽  
M. Tejenosh
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Sensor Fault ◽  
Turbine Generators ◽  
Wind Turbine Generators

Sensor Fault Diagnosis for an UAV Control System Based on a Strong Tracking Kalman Filter

2014 ◽  
Vol 687-691 ◽  
pp. 270-274 ◽  
Author(s):  
Feng Tian ◽  
Jian Yang Zheng ◽  
Tong Zhang
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Fault Diagnosis ◽  
Real Time ◽  
Flight Control ◽  
Joint Estimation ◽  
Filter Method ◽  
State Variables ◽  
Flight Control System ◽  
Sensor Fault

The fault diagnosis of unmanned aerial vehicle (UAV) flight control system is an important research of UAV in health management. The sensor is the link which easiest to have problems of the flight control system. Making timely and accurate prediction of its faults is particularly important. A strong tracking Kalman Filter method for the sensor fault diagnosis of UAV flight control system was presented in this paper. The parameters of the system were extended to the state variables, the sensor fault observer was constructed, and the joint estimation of states and parameters of flight control system were gotten. The method can be used to real-time estimate the unmeasured states and time-varying parameters. The results of simulation experiments show that the method has a good real-time and accuracy in the sensor fault diagnosis of flight control system.

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