Detection of broken rotor bars in induction motors using nonlinear Kalman filters.

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.

[1]  C. C. Chan,et al.  An effective method for rotor resistance identification for high-performance induction motor vector control , 1990 .

[2]  R. Beguenane,et al.  Induction motors thermal monitoring by means of rotor resistance identification , 1997, 1997 IEEE International Electric Machines and Drives Conference Record.

[3]  N. Nait-Said.  Rotor Resistance Estimation of an Induction Motor to Detect Broken Bars Fault Using H-H Method , 2004 .

[4]  J. Lang,et al.  Detection of broken rotor bars in induction motors using state and parameter estimation , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[5]  R. Mehra.  A comparison of several nonlinear filters for reentry vehicle tracking , 1971 .

[6]  R. Wishner,et al.  Suboptimal state estimation for continuous-time nonlinear systems from discrete noisy measurements , 1968 .

[7]  Gerasimos G Rigatos,et al.  Particle and Kalman filtering for state estimation and control of DC motors. , 2009, ISA transactions.

[8]  H. W. Sorenson,et al.  Kalman filtering : theory and application , 1985 .

[9]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[10]  Mohamed Benbouzid,et al.  Induction motors' faults detection and localization using stator current advanced signal processing techniques , 1999 .

[11]  P. Purkait,et al.  Space-Vector Characterization of Induction Motor Operating Conditions , 2008 .

[12]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[13]  G.C. Soukup,et al.  Cause and analysis of stator and rotor failures in 3-phase squirrel cage induction motors , 1991, Conference Record of 1991 Annual Pulp and Paper Industry Technical Conference.

[14]  Kun-Chu Liu.  Model-based failure detection in induction motors using nonlinear filtering , 1995 .

[15]  P. J. Costa.  Adaptive model architecture and extended Kalman-Bucy filters , 1994 .

[16]  L Giovanini,et al.  A fault detection and isolation filter for discrete linear systems. , 2003, ISA transactions.

[17]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[18]  Gerald Burt Kliman,et al.  Methods of Motor Current Signature Analysis , 1992 .

[19]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[20]  Yaakov Bar-Shalom,et al.  Tracking with debiased consistent converted measurements versus EKF , 1993 .

[21]  Peter Vas,et al.  Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines , 1993 .

[22]  M. Ehsani,et al.  Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives , 2006, IEEE/ASME Transactions on Mechatronics.

[23]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[24]  M.E.H. Benbouzid,et al.  H-G diagram based rotor parameters identification for induction motors thermal monitoring , 2000 .

[25]  Cornelius Leondes,et al.  Statistically Linearized Estimation of Reentry Trajectories , 1981, IEEE Transactions on Aerospace and Electronic Systems.

[26]  A. Benchaib,et al.  Detection of broken bars in induction motors using an extended Kalman filter for rotor resistance sensorless estimation , 2000 .