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Invited Talk; International audience; This paper is aimed at exploiting Fault Detection and Isolation (FDI) techniques widely known in automatic control for solving online test problem in embedded Integrated Circuits (ICs). Before reaching this aim, we will briefly review the field of microelectronics testing, introducing basic concepts and techniques. We will next introduce FDI model-based approaches and their application for online testing of embedded ICs considering linear systems with potential faults and disturbances. The parity relation-based residual is specially suitable for this type of application. As an example, we will apply it to concurrent fault detection in a digital embedded filter. The proposed scheme will then be illustrated for a linear digital pass-band elliptic filter.

This paper considers the problem of estimating the parameters of an autoregressive (AR) process in presence of additive white noise and proposes a new identification method, based on theoretical results originally developed in errors-in-variables contexts. This approach allows to estimate the AR parameters, the driving noise variance and the variance of the additive noise in a congruent way in that these estimates assure the positive definiteness of the autocorrelation matrix. The performance of the proposed algorithm is compared with that of bias-compensated least-squares methods by means fo Monte Carlo simulations. The results show the effectivenesss of the new method also in presence of high amounts of noise.

Classical model–based fault detection schemes for linear multivariable systems require the definition of suitable residual functions. This paper shows the possibility of identifying residual generators even when the system model is unknown, by following a black–box approach. The result is obtained by using canonical input–output polynomial forms which lead to characterise in a straightforward fashion the basis of the subspace described by all possible residual generators. The performance of the proposed identification method is tested by means of a Monte Carlo simulation.

Model matching by output feedback is completely treated in the geometric approach framework. Self-bounded controlled invariant subspaces are shown to play a crucial role in the synthesis of minimal-order dynamic regulators achieving model matching by output feedback with stability. The approach provides insight into the internal eigenstructure of the minimal self-bounded controlled invariant subspace, thus paving the way to an effective treatment of nonminimum-phase systems.

The problem of making the output insensitive to an exogenous input signal possibly known with preview is tackled in the geometric approach context. The definition of minimal preview for decoupling is introduced. Necessary and sufficient constructive conditions for decoupling with minimal preview are proved by means of simple geometric arguments. The structural and the stabilizability conditions are considered separately. The minimal complexity of the solution is guaranteed by using the minimal self-bounded controlled invariant subspace. In the presence of unstable dynamics of that subspace, a steering along zeros technique completely devised in the state-space allows the solution with internal stability to be nonetheless achieved. Implementation is obtained by resorting to finite impulse response systems.

The paper deals with condition monitoring of electric switch machines for railway points. The proposed detection system is based on off-line processing of armature current and voltage sampled as the machine operates a switch of railway tracks. The system, consisting of an algorithm tuned on a model of the machine as both the rails are driven towards their rest positions, allows detection of faults of an incremental nature, i.e. those connected to progressive increase of frictional loads (loss of lubrication, deterioration of slide chairs, increasing obstructions, etc). The algorithm realizes a finite impulse response system whose convolution profiles are designed on the basis of an H 2 -norm criterion which guarantees robustness, particularly with respect to electrical noise.

The Errors-in-Variables (EIV) stochastic environment constitutes a superset of most common stochastic environments considered, for instance, in Kalman filtering or in equation-error identification here the process input is assumed as noise-free. Errors-in-variables models assume, on the contrary, the presence of unknown additive noise also on the inputs; the associated filtering procedures concern thus the optimal (minimal variance) estimation not only of the system state and output but also of the input. Optimal EIV filtering has been formulated and solved only recently making reference to SISO models; this paper extends the efficient algorithm proposed recently by the authors, based on the Cholesky factorization, to the more general multivariable case.