S. Beghelli

Diagnosis techniques for sensor faults of industrial processes

A model-based procedure exploiting analytical redundancy for the detection and isolation of faults in input-output control sensors of a dynamic system is presented. The diagnosis system is based on state estimators, namely dynamic observers or Kalman filters designed in deterministic and stochastic environments, respectively, and uses residual analysis and statistical tests for fault detection and isolation. The state estimators are obtained from an input-output data process and standard identification techniques based on ARX or errors-in-variables models, depending on signal to noise ratio. In the latter case the Kalman filter parameters, i.e., the model parameters and input-output noise variances, are obtained by processing the noisy data according to the Frisch scheme rules. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model. Results from simulation show that minimum detectable faults are perfectly compatible with the industrial target of this application.

Parameter identification for piecewise-affine fuzzy models in noisy environment

Abstract In this paper the problem of identifying a fuzzy model from noisy data is addressed. The piecewise-affine fuzzy model structure is used as non-linear prototype for a multi–input, single–output unknown system. The consequents of the fuzzy model are identified from noisy data which are collected from experiments on the real system. The identification procedure is formulated within the Frisch scheme, well established for linear systems, which is extended so that it applies to piecewise-affine, constrained models.

Identification of piecewise affine models in noisy environment

This paper addresses the identification of non-linear systems. A wide class of these systems can be described using non-linear time-invariant regression models, that can be approximated by means of piecewise affine prototypes with an arbitrary degree of accuracy. This work concerns the identification of piecewise affine model parameters through input-output data affected by additive noise. In order to show the effectiveness of the developed technique, the results obtained in the identification of both a simple simulated system and a real dynamic process are reported.

Fault Detection and Isolation Based on Dynamic Observers Applied to Gas Turbine Control Sensors

In order to prevent machine malfunctions and to determine the machine operating state, it is necessary to use correct measurements from actual system inputs and outputs. This requires the use of techniques for the detection and isolation of sensor faults.In this paper an approach based on analytical redundancy which uses dynamic observers is suggested to solve the sensor fault detection and isolation problem for a single-shaft industrial gas turbine. The proposed technique requires the generation of classical residual functions obtained with different observer configurations. The diagnosis is performed by checking fluctuations of these residuals caused by faults.© 1998 ASME

Robust fault diagnosis of dynamic processes using parametric identification with eigenstructure assignment approach

Presents some results concerning robust fault diagnosis of dynamic processes using a parametric identification technique. The first step of the considered approach estimates an equation error model by means of the input-output data acquired from the monitored system. In particular, the equation error term of the model takes into account disturbances, non-linear and time-variant terms, measurement errors, etc. The next step of the method requires a state-space realization of the input-output equation error model which allows us to define an equivalent disturbance distribution matrix related to the error term. Therefore, the eigenstructure assignment results for robust fault diagnosis can be successfully applied. The proposed procedure has been tested by means of a industrial process simulator. In such a manner, sensor, component and actuator faults can be simulated on an single shaft gas turbine. Results from this simulator are also reported.

Parameters identification for piecewise linear models with weakly varying noise

This paper concerns the identification of piecewise linear models from noisy data. The identification procedure is formulated within the Frisch scheme (1934), a technique well established for linear systems.

Nonlinear algebraic system identification via piecewise affine models in stochastic environment

The paper discusses the issue of nonlinear system modelling by means of continuous piecewise affine models identified from noisy data. Based on the assumption that a piecewise affine system can approximate with arbitrary degree of accuracy any nonlinear relation, the identification problem is formulated within the Frisch scheme (R. Frisch, 1934). This identification procedure is well established for affine systems and here modified and improved to be applied to piecewise affine system identification. The simulation results of a test case (the identification of a continuous piecewise affine function) shows that an effective identification method has been achieved.

Identification and fault diagnosis of nonlinear dynamic processes using hybrid models

This work addresses a novel approach for fault diagnosis of industrial processes using hybrid models. A nonlinear dynamic process can, in fact, be described as a composition of different affine sub-models selected according to the process operating conditions. This paper deals with the identification of hybrid model parameters through input-output data affected by additive noise. The fault detection scheme adopted to generate residuals uses the estimated hybrid model. In order to show the effectiveness of the developed technique, the results obtained in the fault diagnosis of a real industrial plant are reported.

Rank Reducibility of a Covariance Matrix in the Frisch Scheme

Abstract The Frisch scheme for identification of mathematical models from data corrupted by additive noise contains many unsolved aspects. One of the principal problems, of particular interest for factor analysis and structural regression methodologies, concerns rank reducibility of a covariance matrix simply by changing its diagonal entries. With reference to this topic, the paper shows that the mathematical models compatible with the data are the solutions of a set of polynomial equations which satisfy some well-defined constraints. The approach is based on the rank reducibility criteria suggested in a well-known paper by Ledermann, generalized to take into account the definiteness conditions on the noise-free covariance matrix. The results obtained give a deeper insight on the theoretical properties of the Frisch scheme and can represent a starting point for the design of numerical algorithms to solve the problem.

The Frisch Identification Scheme: Properties of the Solution in the Dynamic Case

Abstract This paper investigates some of the many algebraic properties of the solution of the Frisch identification scheme applied to dynamic systems. These properties are related to the design of a robust selection criterion leading to a single model also when the assumptions of the scheme are not fulfilled.