Fault detection in electric motors by means of the extended Kalman Filter as disturbance estimator

2014 ◽  
Author(s):  
Alejandro J. Fernandez Gomez ◽  
Victor H. Jaramillo ◽  
James R. Ottewill
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Electric Motors ◽  
Disturbance Estimator

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

Investigating Fault Detection and Diagnosis in a Hydraulic Pitch System Using a State Augmented EKF-Approach

2019 ◽  
Author(s):  
Magnus F. Asmussen ◽  
Henrik C. Pedersen ◽  
Lina Lilleengen ◽  
Andreas Larsen ◽  
Thomas Farsakoglou
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
System Parameters ◽  
Power Regulation ◽  
Pitch System ◽  
Detection And Diagnosis ◽  
Drop Outs

Abstract Pitch systems impose an important part of today’s wind turbines, where they are both used for power regulation and serve as part of a turbines safety system. Any failure on a pitch system is therefore equal to an increase in downtime of the turbine and should hence be avoided. By implementing a Fault Detection and Diagnosis (FDD) scheme faults may be detected and estimated before resulting in a failure, thus increasing the availability and aiding in the maintenance of the wind turbine. The focus of this paper is therefore on the development of a FDD algorithm to detect leakage and sensor faults in a fluid power pitch system. The FDD algorithm is based on a State Augmented Extended Kalman Filter (SAEKF) and a bank of observers, which is designed utilizing an experimentally validated model of a pitch system. The SAEKF is designed to detect and estimate both internal and external leakage faults, while also estimating the unknown external load on the system, and the bank of observers to detect sensor drop-outs. From simulation it is found that the SAEKF may detect both abrupt and evolving internal and external leakages, while being robust towards noise and variation in system parameters. Similar it is found that the scheme is able to detect sensor drop-outs, but is less robust towards this.

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