Fault detection and isolation based on nonlinear analytical redundancy applied to an induction machine

This paper deals with the design of a fault detection and isolation (FDI) of the sensors for an induction machine using nonlinear analytical redundancy (NLAR). This paper investigates the detection and isolation of faults using elimination of unknown variables of the system and in particularly the unknown system states. The induction machine (IM), it is highly nonlinear, multivariable, time-varying system and particularly when subject to the faults, it is difficult to detected them by linear approaches. The nonlinear parity space algorithm is able to detect and isolate sensor faults such IM speed and stator currents or actuator faults (stator voltage). In order to prove the accuracy of approaches studying in this work, simulation results will be given.

[1]  M. Chaabane,et al.  Takagi-Sugeno fuzzy sensor faults estimation of an induction motor , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[2]  M. A. Ouhrouche.  Estimation of speed, rotor flux, and rotor resistance in cage induction motor using the EKF algorithm , 2002 .

[3]  A. Hamzaoui,et al.  A fuzzy model reference adaptive system control for induction motor drives , 2013, 3rd International Conference on Systems and Control.

[4]  Daniel U. Campos-Delgado,et al.  Fault-tolerant control in variable speed drives: a survey , 2008 .

[5]  Haitham Abu-Rub,et al.  Application of Speed and Load Torque Observers in High-Speed Train Drive for Diagnostic Purposes , 2009, IEEE Transactions on Industrial Electronics.

[6]  B. Ould Bouamama,et al.  Fault detection and isolation of PEM fuel cell system based on nonlinear analytical redundancy: An application via parity space approach , 2011 .

[7]  Qinghua Zhang,et al.  Nonlinear system fault diagnosis based on adaptive estimation , 2004, Autom..

[8]  Dominic Gross,et al.  Design of sliding mode observers for TS fuzzy systems with application to disturbance and actuator fault estimation , 2010, 49th IEEE Conference on Decision and Control (CDC).

[9]  Demba Diallo,et al.  Advanced Fault-Tolerant Control of Induction-Motor Drives for EV/HEV Traction Applications: From Conventional to Modern and Intelligent Control Techniques , 2007, IEEE Transactions on Vehicular Technology.

[10]  T.-J. Ho,et al.  Design of a hybrid PID plus fuzzy controller for speed control of induction motors , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[11]  Malek Ghanes,et al.  Backstepping fault tolerant control based on second order sliding mode observer: Application to induction motors , 2011, IEEE Conference on Decision and Control and European Control Conference.

[12]  M. Bayart,et al.  Sensor and actuator fault detection and isolation using two model based approaches: Application to an autonomous electric vehicle , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[13]  Djamel Benazzouz,et al.  A new method to enhance of fault detection and diagnosis in gearbox systems , 2017 .

[14]  A. Hamzaoui,et al.  Diagnostic of Sensors for Induction Machine Powered by Photovoltaic Generator Based on Fuzzy Logic Techniques , 2016 .

[15]  Haitham Abu-Rub,et al.  Speed and Load Torque Observer Application in High-Speed Train Electric Drive , 2010, IEEE Transactions on Industrial Electronics.

[16]  Joseph R. Cavallaro,et al.  Fault residual generation via nonlinear analytical redundancy , 2005, IEEE Transactions on Control Systems Technology.

[17]  D. Mahinda Vilathgamuwa,et al.  A Sensor Fault Detection and Isolation Method in Interior Permanent-Magnet Synchronous Motor Drives Based on an Extended Kalman Filter , 2013, IEEE Transactions on Industrial Electronics.

[18]  Torsten Jeinsch,et al.  Dynamic nonlinear unknown input observer for fault detection of induction motors , 2015, 2015 23rd Iranian Conference on Electrical Engineering.

[19]  Dingli Yu,et al.  Extension of the parity-space method to fault diagnosis of bilinear systems , 2001, Int. J. Syst. Sci..

[20]  S. Drid,et al.  A comparative study of rotor flux estimation in induction motor with a linear parameter varying observer and Kalman Filter , 2014, 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).