Sensor Fault Isolation in a Liquid Flow Process Using Kalman Filter

Sensor fault estimation and isolation is significant for an attitude control systems model of a satellite, as it works in a complex environment. The standard unscented Kalman filter algorithm may lose its accuracy when the noise is considerable. Therefore, an adaptive filtering algorithm is proposed based on the sampled-data descriptor model. The performance of the unscented Kalman filter in sensor fault estimation is improved by the adaptive algorithm depending on innovation and the measurement residual, and its convergence is guaranteed. Combining the adaptive unscented Kalman filter with the multiple-model adaptive estimation, a sensor fault isolation method is proposed. Finally, simulation examples show that this algorithm has better estimating accuracy and isolation results.

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Sensor-fault tolerance using robust MPC with set-based state estimation and active fault isolation

International Journal of Robust and Nonlinear Control ◽

10.1002/rnc.3627 ◽

2016 ◽

Vol 27 (8) ◽

pp. 1260-1283 ◽

Cited By ~ 10

Author(s):

Feng Xu ◽

Sorin Olaru ◽

Vicenc Puig ◽

Carlos Ocampo-Martinez ◽

Silviu-Iulian Niculescu

Keyword(s):

Fault Tolerance ◽

State Estimation ◽

Active Fault ◽

Fault Isolation ◽

Sensor Fault

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Incipient Voltage Sensor Fault Isolation for Rectifier in Railway Electrical Traction Systems

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2017.2696463 ◽

2017 ◽

Vol 64 (8) ◽

pp. 6763-6774 ◽

Cited By ~ 21

Author(s):

Kangkang Zhang ◽

Bin Jiang ◽

Xing-Gang Yan ◽

Zehui Mao

Keyword(s):

Fault Isolation ◽

Voltage Sensor ◽

Sensor Fault

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

IET Electric Power Applications ◽

10.1049/iet-epa.2012.0308 ◽

2013 ◽

Vol 7 (7) ◽

pp. 607-617 ◽

Cited By ~ 50

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

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Application of the REx5 Expert System to Condition Monitoring of the Simple Liquid Flow Process Control Trainer

Applied Condition Monitoring - Advances in Technical Diagnostics ◽

10.1007/978-3-319-62042-8_29 ◽

2017 ◽

pp. 323-334

Author(s):

Sebastian Rzydzik ◽

Krzysztof Psiuk ◽

Marcin Amarowicz ◽

Tomasz Rogala

Keyword(s):

Expert System ◽

Process Control ◽

Condition Monitoring ◽

Liquid Flow ◽

Simple Liquid ◽

Flow Process

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An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4023902 ◽

2013 ◽

Vol 135 (7) ◽

Cited By ~ 18

Author(s):

Donald L. Simon ◽

Jeffrey B. Armstrong

Keyword(s):

Kalman Filter ◽

Engine Performance ◽

Aircraft Engine ◽

Fault Isolation ◽

Performance Estimation ◽

Estimation Accuracy ◽

Fault Diagnostics ◽

Tuning Parameters ◽

Performance Deterioration ◽

Health Parameters

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

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