Sensor Fault Diagnosis of Maglev Train Based on Kalman Filter Group

2012 ◽  
Author(s):  
Song Xue ◽  
Zhiqiang Long ◽  
Guang He ◽  
Ning He
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Sensor Fault ◽  
Maglev Train ◽  
Filter Group

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.

scholarly journals Sensor Fault Diagnosis of Locomotive Electro-Pneumatic Brake Using an Adaptive Unscented Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dianzhu Gao ◽  
Jun Peng ◽  
Yunyou Lu ◽  
Rui Zhang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Pressure Sensor ◽  
Unscented Kalman Filter ◽  
Brake System ◽  
Normal Operation ◽  
Strong Nonlinearity ◽  
Reservoir Pressure ◽  
Sensor Fault ◽  
Adaptive Unscented Kalman Filter

Normal operation of the pressure sensor is important for the safe operation of the locomotive electro-pneumatic brake system. Sensor fault diagnosis technology facilitates detection of sensor health. However, the strong nonlinearity and variable process noise of the brake system make the sensor fault diagnosis become challenging. In this paper, an adaptive unscented Kalman filter- (UKF-) based fault diagnosis strategy is proposed, aimed at detecting bias faults and drift faults of the equalizing reservoir pressure sensor in the brake system. Firstly, an adaptive UKF based on the Sage-Husa method is applied to accurately estimate the pressure transients in the equalizing reservoir of the brake system. Then, the residual is generated between the estimated pressure by the UKF and the measured pressure by the sensor. Afterwards, the Sequential Probability Ratio Test is used to evaluate the residual so that the incipient and gradual sensor faults can be diagnosed. An experimental prototype platform for diagnosis of the equalizing reservoir pressure control system is constructed to validate the proposed method.

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