Sensor Fault Diagnosis for Unmanned Quadrotor Helicopter via Adaptive Two-Stage Extended Kalman Filter

2017 ◽  
Author(s):  
Yujiang Zhong ◽  
Wei Zhang ◽  
Youmin Zhang
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Sensor Fault ◽  
Two Stage ◽  
Unmanned Quadrotor Helicopter ◽  
Quadrotor Helicopter

Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives

2013 ◽  
Vol 7 (7) ◽  
pp. 607-617 ◽  
Author(s):  
Xinan Zhang ◽  
Gilbert Foo ◽  
Mahinda Don Vilathgamuwa ◽  
King Jet Tseng ◽  
Bikramjit Singh Bhangu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Motor Drives ◽  
Sensor Fault ◽  
Induction Motor Drives ◽  
System Reconfiguration ◽  
Sensor Fault Detection

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Optimal Two-Stage UKF

2018 ◽  
Vol 36 (5) ◽  
pp. 933-941
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Degang Huang ◽  
Huakun Chen ◽  
Jinglong Liu
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Two Stage ◽  
Inertial Measurement ◽  
System States

Optimal two stage Kalman filter (OTSKF) is able to obtain optimal estimation of system states and bias for linear system which contains random bias. Unscented Kalman filter (UKF) is a conventional nonlinear filtering method which utilizes Sigmas point sampling and unscented transformation technology realizes propagation of state means and covariances through nonlinear system. Aircraft is a typical complicate nonlinear system, this paper treats the faults of Inertial Measurement Unit (IMU) as random bias, established a filtering model which contains faults of IMU. Hybird the two stage filtering technique and UKF, this paper proposed an optimal two stage unscented Kalman filter (OTSUKF) algorithm which is suitable for fault diagnosis of IMU, realized optimal estimation of system states and faults identification of IMU via proposed innovative designing method of filtering model and the algorithm was validated that it is robust to wind disterbance via real flight data and it is also validated that proposed OTSUKF is optimal in the existance of wind disturbance via comparing with the existance iterated optimal two stage extended kalman filter (IOTSEKF) method.

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF

2020 ◽  
Vol 38 (4) ◽  
pp. 806-813
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Xiaoxiong Liu ◽  
Weinan Li
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Random Walk Model ◽  
Measurement Unit ◽  
Actual Situation ◽  
Two Stage ◽  
Inertial Measurement

In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.

Concurrent Fault Diagnosis Based on an Extended Kalman Filter

2021 ◽  
Author(s):  
Adrian Lizarraga ◽  
Ofelia Begovich ◽  
Antonio Ramirez
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter

scholarly journals Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2972 ◽  
Author(s):  
Waseem El Sayed ◽  
Mostafa Abd El Geliel ◽  
Ahmed Lotfy
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Continuous Monitoring ◽  
Unscented Kalman Filter ◽  
Fault Tolerant ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Practical Implementation ◽  
Complete Failure

Since the permeant magnet synchronous generator (PMSG) has many applications in particular safety-critical applications, enhancing PMSG availability has become essential. An effective tool for enhancing PMSG availability and reliability is continuous monitoring and diagnosis of the machine. Therefore, designing a robust fault diagnosis (FD) and fault tolerant system (FTS) of PMSG is essential for such applications. This paper describes an FD method that monitors online stator winding partial inter-turn faults in PMSGs. The fault appears in the direct and quadrature (dq)-frame equations of the machine. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) were used to detect the percentage and the place of the fault. The proposed techniques have been simulated for different fault scenarios using Matlab®/Simulink®. The results of the EKF estimation responses simulation were validated with the practical implementation results of tests that were performed with a prototype PMSG used in the Arab Academy For Science and Technology (AAST) machine lab. The results showed impressive responses with different operating conditions when exposed to different fault states to prevent the development of complete failure.

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