Isolation of parametric faults in continuous-time multivariable systems: a sampled data-based approach

A novel approach is proposed towards on-line and real-time detection and isolation of parametric faults in a multivariable linear continuous-time (CT) system. The problem of fault detection and isolation (FDI) is formulated in terms of a CT state space model. Since parameters in a CT model usually have simple relationships with physical parameters of the system, isolating parametric faults in the CT model can lead to the isolation of undesired changes in the physical parameters. Isolating parametric faults is very challenging, because even in a linear time-invariant system, the fault model can be time-varying and random. To obtain a constant fault model, many existing parametric FDI schemes have to make unrealistical assumptions. Our proposed FDI approach can generate an optimal primary residual vector (PRV), in which the fault model is constant without making any assumptions. To isolate faults, the PRV is transformed into a set of structured residual vectors (SRVs), where one SRV is made insensitive to a specified subset of faults, but most sensitive to other faults. The proposed approach is successfully applied to detection and isolation of undesired changes in the physical parameters of a simulated continuously stirred tank process.

[1]  Weihua Li,et al.  Subspace identification of continuous time models for process fault detection and isolation , 2003 .

[2]  Theodore E. Cohn,et al.  Detection and identification , 1985 .

[3]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[4]  J. Gertler,et al.  Optimal residual decoupling for robust fault diagnosis , 1995 .

[5]  M. Nyberg.  Criterions for detectability and strong detectability of faults in linear systems , 2000 .

[6]  Luis E. Garza-Castañón,et al.  Integration of Fault Detection and Diagnosis in a Probabilistic Logic Framework , 2002, IBERAMIA.

[7]  Michèle Basseville,et al.  Early warning of slight changes in systems , 1994, Autom..

[8]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[9]  Sirish L. Shah,et al.  Structured residual vector-based approach to sensor fault detection and isolation , 2002 .

[10]  Marcel Staroswiecki,et al.  Comparison of multivariable identification and parity space techniques for FDI purpose in M.I.M.O systems , 1999, 1999 European Control Conference (ECC).

[11]  Michèle Basseville,et al.  Subspace-based fault detection algorithms for vibration monitoring , 2000, Autom..

[12]  Wojciech Cholewa,et al.  Fault Diagnosis , 2004, Springer Berlin Heidelberg.

[13]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[14]  Michèle Basseville,et al.  Detection of Abrupt Changes: Theory and Applications. , 1995 .

[15]  Michel Verhaegen,et al.  Continuous-time identification of SISO systems using Laguerre functions , 1999, IEEE Trans. Signal Process..

[16]  S. Qin,et al.  Detection and identification of faulty sensors in dynamic processes , 2001 .

[17]  David Singer,et al.  Augmented Models for Statistical Fault Isolation in Complex Dynamic Systems , 1985, 1985 American Control Conference.

[18]  A. H. Whitfield,et al.  Integral-equation approach to system identification , 1987 .

[19]  Michèle Basseville,et al.  On-board Component Fault Detection and Isolation Using the Statistical Local Approach , 1998, Autom..

[20]  Youmin Zhang,et al.  Active fault-tolerant control system against partial actuator failures , 2002 .

[21]  Weihua Li,et al.  Detection, identification, and reconstruction of faulty sensors with maximized sensitivity , 1999 .

[22]  G. Favier,et al.  Numerical integration approach to on-line identification of continuous-time systems , 1990 .

[23]  Michèle Basseville,et al.  The asymptotic local approach to change detection and model validation , 1987 .

[24]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

[25]  R. Isermann.  INTEGRATION OF FAULT DETECTION AND DIAGNOSIS METHODS , .

[26]  Michel Verhaegen,et al.  Stochastic theory of continuous-time state-space identification , 1999, IEEE Trans. Signal Process..

[27]  R. Guidorzi.  Canonical structures in the identification of multivariable systems , 1975, Autom..

[28]  R. Doraiswami,et al.  An intelligent sensor to monitor power system stability, performance and diagnose failures , 1990 .

[29]  Roberto Guidorzi,et al.  A new input-output canonical form for multivariable systems , 1976 .

[30]  Setsuo Sagara,et al.  Numerical integration approach to on-line identification of continuous-time systems , 1990, Autom..

[31]  Jakob Stoustrup,et al.  Fault Detection and Isolation in Systems with Parametric Faults , 1999 .

[32]  S. Joe Qin,et al.  Consistent dynamic PCA based on errors-in-variables subspace identification , 2001 .

[33]  Michel Verhaegen,et al.  Fault detection: a subspace identification approach , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[34]  Peter J. Gawthrop,et al.  On the estimation of continuous time transfer functions , 2001 .

[35]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[36]  Rolf Johansson,et al.  Identification of continuous-time models , 1994, IEEE Trans. Signal Process..

[37]  Hiroshi Oku,et al.  Change detection in the dynamics with recursive subspace identification , 2001 .

[38]  Janos Gertler,et al.  A new structural framework for parity equation-based failure detection and isolation , 1990, Autom..