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Sensors 2015, 15(2), 4578-4591; doi:10.3390/s150204578

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

1,2,3
,
1,2,3,* , 1,2,3
and
1,2,3
1
College of Information Engineering, Capital Normal University, Beijing 100048, China
2
Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China
3
Beijing Key Laboratory of Electronic System Reliable Technology, Capital Normal University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Received: 2 December 2014 / Revised: 7 February 2015 / Accepted: 9 February 2015 / Published: 16 February 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [913 KB, uploaded 16 February 2015]   |  

Abstract

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. View Full-Text
Keywords: cubature Kalman filter; fault detection; strong tracking; sensor cubature Kalman filter; fault detection; strong tracking; sensor
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, L.; Wu, L.; Guan, Y.; Wang, G. Online Sensor Fault Detection Based on an Improved Strong Tracking Filter. Sensors 2015, 15, 4578-4591.

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