On-line identification and robust fault diagnosis for nonlinear PMSM drives

The performance of model-based fault diagnosis (FD) algorithms is degraded by modeling errors caused by process uncertainties. FD robustness against process uncertainties is discussed in this paper. In the proposed FD schematics, robustness is improved by using adaptive residual generators, in which the advanced on-line identification plays a key role. The approach is investigated on a permanent magnet synchronous motor drive system with inherent nonlinearity and time-varying parameters caused by process uncertainties. Stator resistance and q-axis inductance are considered as possible sources of system uncertainties under different operating conditions. Mathematical decoupling simplifies both identification and fault detection designs for the drive monitoring process. Preliminary simulation results are provided.

[1]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[2]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[3]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[4]  Guanrong Chen,et al.  Bifurcations and chaos in a permanent-magnet synchronous motor , 2002 .

[5]  Jens G. Balchen,et al.  Elementary nonlinear decoupling control of composition in binary distillation columns , 1995 .

[6]  Erik Frisk,et al.  Order of Residual Generators - Bounds and Algorithms , 2000 .

[7]  Jens G. Balchen,et al.  Elementary Nonlinear Decoupling Control of Composition in Binary Distillation Columns , 1994 .

[8]  M. Azizur Rahman,et al.  Analysis of brushless permanent magnet synchronous motors , 1996, IEEE Trans. Ind. Electron..

[9]  F. Meibody-Tabar,et al.  On-line identification of PMSM electrical parameters based on decoupling control , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[10]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[11]  R. Krishnan,et al.  Electric Motor Drives: Modeling, Analysis, and Control , 2001 .

[12]  R. Isermann,et al.  Fault detection based on adaptive parity equations and single-parameter tracking , 1996 .

[13]  D. C. Hamill,et al.  Instability, subharmonics and chaos in power electronic systems , 1989 .

[14]  Zi-Li Deng,et al.  Multivariable decoupling pole assignment self-tuning feedforward controller , 1991 .

[15]  E. A. Woods,et al.  Fault detection, supervision and safety for technical processes , 1993 .