Title :
Induction Machines: A Novel, Model based Non-invasive Fault Detection and Diagnosis Technique
Author :
Padmakumar, S. ; Roy, Kallol ; Agarwal, Vivek
Author_Institution :
Dept. of Atomic Energy, BARC, Mumbai
Abstract :
Model based fault detection and diagnosis in induction motor is gaining importance as it can take care of model and measurement uncertainties with the help of variants of Kalman Filters. A study of such a methodology and the potential to apply the same online is discussed. Mainly soft faults are considered for this work and MATLAB simulation results are presented. The data generation, filter convergence issues, hypothesis testing, generalized likelihood estimates etc. are addressed. A SIMLINK model is used for data generation and various types of faults are introduced. An extended Kalman filter using MATLAB is run to detect the changes.
Keywords :
Kalman filters; fault diagnosis; induction motors; data generation; extended Kalman filter; hypothesis testing; induction machines; induction motor; noninvasive fault detection; noninvasive fault diagnosis technique; Convergence; Fault detection; Fault diagnosis; Filters; Induction machines; Induction motors; MATLAB; Mathematical model; Measurement uncertainty; Testing; Extended Kalman Filter; Fault detection and diagnosis; Induction motor model;
Conference_Titel :
Power System Technology and IEEE Power India Conference, 2008. POWERCON 2008. Joint International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-1-4244-1763-6
Electronic_ISBN :
978-1-4244-1762-9
DOI :
10.1109/ICPST.2008.4745282
