Diagnosis and Estimation of Sensor Bias in Dynamic Systems Using Adaptive Techniques

Abstract:

The estimation and detection of sensor bias fault in a dynamic system is discussed. The development of more general methodology for diagnosing sensor bias faults in a dynamic system which depends on the state of the system is established. The non linearity in the system nominal model is assumed to be the function of input and output only, this note used the adaptive technique of model based system and Kaczmarz’s Projection algorithm. An Observer is designed to generate residual of the system which indicates the presence of fault and initiates to estimate unknown sensor bias in the presence of model uncertainties. The tank level system is considered for simulative example is presented to illustrate the methodology. The robustness, sensitivity and stability properties of the dynamic system were analyzed.

• Periodical: Applied Mechanics and Materials (Volume 678)
• Pages: 313-317
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.678.313
• Online since: October 2014
• Authors: R. Aarthi*, R. Ananda Natarajan
• Keywords: Adaptive Technique, Analytical Properties, Kaczmarz’s Projection Algorithm, Sensor Bias

References

[1] Youmin Zhang, Jin Jiang, Bibliographical review on reconfigurable fault-tolerant control systems,. Annual Reviews in Control 32 (2008) 229–252.

DOI: 10.1016/j.arcontrol.2008.03.008

[2] Venkat Venkatasubramanian, Raghunathan Rengaswamy, Surya N. Kavuri, Kewen Yin, A review of process fault detection and diagnosis Part III: Process history based methods, Computers and Chemical Engineering 27 (2003), pg. 327-/346.

DOI: 10.1016/s0098-1354(02)00162-x

[3] Venkat Venkatasubramanian, Raghunathan Rengaswamy, Kewen Yin, Surya N. Kavuri, A review of process fault detection and diagnosis Part I: Quantitative model-based methods,. Computers and Chemical Engineering 27 (2003) 293-311.

DOI: 10.1016/s0098-1354(02)00160-6

[4] Dr. Ron J. Patton, Mr. J. Chen, Robustness In Quantitative Model-Based Fault Diagnosis,. Department of Electronics, University of York, YORK, YO1 SDD, England, pg. 4/1-4/17.

[5] Venkat Venkatasubramanian, Raghunathan Rengaswamy, Surya N. Kavuri, A review of process fault detection and diagnosis Part II: Qualitative models and search strategies,. Computers and Chemical Engineering 27 (2003) 313-/326.

DOI: 10.1016/s0098-1354(02)00161-8

[6] Janos Gertler"Residual Generation from Principal Component Models for Fault Diagnosis in Linear Systems Part II: Extension to optimal residuals and dynamic systems" Proc. of the 2005 IEEE International Symposium on Intelligent Control Limassol, Cyprus, June 27-29, (2005).

DOI: 10.1109/.2005.1467088

[7] X. Zhang "Sensor bias Fault detection and isolation in a class of non linear uncertain system using adaptive estimation, Automatic Control, IEEE transactions, (2011).

DOI: 10.1109/tac.2011.2112471

[8] R. Aarthi and R. Anantha Natarajan Estimation of Multiple Transient Actuator Faults Using Augmented Error Technique, International Journal of Automation an Autonomous system, vol 5, No4, July (2013).

[9] Mhamdi. L, Dhouibi. H, and Liouane. N"Multiple fault Diagnosis using Mathematical Model" Control Conference (ASCC), 2013 9th Asian Pg: 1-6, June (2013).

DOI: 10.1109/ascc.2013.6606344