Fault Diagnosis and Fault-Tolerant Control in Linear Drives Using the Kalman Filter

In this paper, we consider the fault diagnosis and fault-tolerant problem for a linear drive system subject to system noise. First, we propose a residual generator based on the Kalman filter, which can be used to detect if a failure occurs. Second, two Kalman filters are designed to diagnose the fault type. Third, when a fault is diagnosed, the fault-tolerant control is used to accommodate this failure. Finally, the proposed method is tested in a real linear drive system.

[1]  Mickaël Hilairet,et al.  Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter , 2009, Autom..

[2]  Nariman Sepehri,et al.  A Wavelet-Based Approach to Internal Seal Damage Diagnosis in Hydraulic Actuators , 2010, IEEE Transactions on Industrial Electronics.

[3]  Maorong Weng Intelligent Diagnosis Techniques in Automotive Engines Fault Based on Fuzzy Support Vector Machine , 2010, 2010 Asia-Pacific Conference on Wearable Computing Systems.

[4]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[5]  Jianzhong Fu,et al.  Intelligent fault diagnosis using rough set method and evidence theory for NC machine tools , 2009, Int. J. Comput. Integr. Manuf..

[6]  Mickaël Hilairet,et al.  Frequency estimation for sensorless control of induction motors , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[7]  Xiaoli Ma,et al.  Adaptive state feedback control of systems with actuator failures , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[8]  Yixin Diao,et al.  Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine , 2001, IEEE Trans. Control. Syst. Technol..

[9]  Thomas G. Habetler,et al.  A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems , 2008, IEEE Transactions on Industrial Electronics.

[10]  Murat Barut,et al.  Bi Input-extended Kalman filter based estimation technique for speed-sensorless control of induction motors , 2010 .

[11]  Bernardo Tormos,et al.  Detection and Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines , 2010, IEEE Transactions on Industrial Electronics.

[12]  Rolf Isermann,et al.  Application of model-based fault detection to a brushless DC motor , 2000, IEEE Trans. Ind. Electron..

[13]  Ming Cong,et al.  Wafer Pre-Aligner System Based on Vision Information Processing , 2007 .

[14]  C. W. de Silva,et al.  Tool wear detection and fault diagnosis based on cutting force monitoring , 2007 .

[15]  Kok Kiong Tan,et al.  Automated Fault Detection and Diagnosis in Mechanical Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Paul M. Frank,et al.  Fault Diagnosis in Dynamic Systems , 1993, Robotics, Mechatronics and Manufacturing Systems.

[17]  Hubert Razik,et al.  Eccentricity Fault Diagnosis of a Dual-Stator Winding Induction Machine Drive Considering the Slotting Effects , 2008, IEEE Transactions on Industrial Electronics.

[18]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[19]  Ramli Adnan,et al.  A novel integrated sensor system for indoor air quality measurement , 2009, 2009 5th International Colloquium on Signal Processing & Its Applications.

[20]  Vicenç Puig,et al.  Passive Robust Fault Detection of Dynamic Processes Using Interval Models , 2008, IEEE Transactions on Control Systems Technology.

[21]  Khaled Jelassi,et al.  An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor , 2008, IEEE Transactions on Industrial Electronics.