Application of Least Squares Parameter Estimation Techniques in Fault Detection
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2013 by IJETT Journal | ||
| Volume-4 Issue-3 |
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| Year of Publication : 2013 | ||
| Authors : Absal Nabi |
Citation
Absal Nabi . "Application of Least Square s Parameter Estimation Techniques in Fault Detection". International Journal of Engineering Trends and Technology (IJETT). V4(3):447-450 Mar 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.
Abstract
Failure detection has been the subject of many studies in the past. Modern technology has required highly complex dynamic systems. A critical system is any system whose ‘failure’ could threaten the system’s environment or the existence of the organization which operates the system. A fault is understood as any kind of malfunction in the actual dynamic system, the plant that leads to an unacceptable anomaly in the overall system performance. Fault detection via parameter estimat ion relies in the principle that possible faults in the monitored system can be associated with specific parameters and states of the mathematical model of the system given in the form of an input - output relation. In this thesis, the focus is put on the st udy of fast least squares parameter estimation methods, like recursive least square (RLS) algorithm, Fast Kalman algorithm, FAEST(fast a priori error sequential technique) algorithm, FTF(fast transversal filter) algorithm and lattice filter algorithm and t heir fast algorithm implementation. The above algorithms are applied to a dynamic system and the performances of different algorithms in detecting different changes in the systems are compared. The MATLAB coding of these algorithms are done and their effec t on first and second order dynamic systems under various conditions are verified. Statistical methods like Shewart moving range control chart, the cumulative sum control chart, the moving average control chart, the exponentially weighted moving average co ntrol chart (EWMA) etc. for analyzing the changes in dynamic systems are also studied.
References
[1] George Vachtsevanos, Intelligent Fault Diagnosis and Prognosis For Engineering Ststems , First Edition , John Wiley&Sons , Inc, 2006
[2] Otto S., and Denier J.P., an Intr oduction to Programming And Numerical Methods in MAT LAB , Low Price Edition, New Age International , 2006
[3] S.Haykin , Adaptive Filter Theory , Prentice - Hall , Second edition1986
[4] C.Hajivev , F.Caliskan , Faul t Diagnosis and Reconfiguration in Flight Control Systems , First Edition, Springer Publishers , 2003
[5] Janos Gertler , Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker Publishers , 1998
[6] Alexander Ilin, Harri Valpola, and Erkki Oja , Nonlinear Dynamica Factor Analysis for State Chang e Detection, IEE E Transactions on Neural Networks, Vol. 15, No. 3, pages 559 - 575, May 1997
Keywords
Fault Detection, Parameter Estimation, Recursive Least Squares, Fast Least Squares , Statistical Control Chart.