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A new actuator fault diagnosis and estimation approach is proposed for dynamical systems. The main contribution consists in enhancing the fault detection with a new observer that takes into account the relative degree of the output of the system with respect to the fault. The Single Input Single Fault (SIFO) case is considered to present the approach and an extension to systems with multiple outputs and multiple faults. The convergence of the proposed residual generator is analyzed using the Lyapunov theory which can be expressed straightforwardly in terms of Linear Matrix Inequalities (LMIs). Numerical examples are provided in order to illustrate the proposed approach.

In this paper, the problem of observers for Takagi-Sugeno (TS) models with unmeasurable premise variables is investigated and a new design approach is proposed. The idea, is motivated by the immersion techniques and auxiliary dynamics generation, and consists in the transformation of the TS system with state dependent weighting functions in a new TS system with weighting functions depending only on the measured variables. This result aims to relax the strong conditions used in the design of observers for TS systems with unmeasurable premise variables. Illustrative example is provided to discuss the performances of the proposed approach.

This paper addresses the design of Unknown Input Observer (UIO) for Linear Parameter Varying (LPV). It is shown that even if the rank conditions ensuring the existence of an UIO are satisfied, the design may fail, due to the polytopic representation of the LPV system. In this paper a new design approach is proposed via a modified UIO for a class of LPV systems with parameter dependent output equation. It is based on parameter algebraic matrix equations and LMI conditions. Examples are provided in order to illustrate the performances of the proposed approach.

In this paper, a new formulation of the problem of mass and energy balance equilibration in the case of unknown-but-bounded errors is proposed. The bounds of the errors are specified over both a measurement noise and the balance equations. Both bounds are mainly motivated by experimental considerations of the measurement precision; with a more general interpretation, they can be considered as parameters that the user has to adjust to make the reconciliation possible. The method is particularly suitable for linear models but has been extended to nonlinear ones as well. Simulations provide results that compare favorably with those of classical reconciliation methods involving maximum likelihood estimation based on statistical knowledge of the measurement errors.

This paper is concerned with the problem of data validation of an instrumentation system. The fault diagnosis method used for the validation is based on the principle of the parity space approach. Residuals are generated thanks to the Analytical Redundancy Relations ARR given by the model and the important number of sensors. The additional concept of residuals structuration is necessary to isolate the detected faults. Finally, data validation task consists in isolating the failing data and in sending only the valid information to control system. An application to an aerospace system illustrates the proposed algorithm.

Dimerization of salicylaldehydes provided 6H,12H-6,12-epoxydibenzo[b,f][1,5]dioxocins in multigram quantities. Deprotonation−allylation of the benzylic acetals followed by further functionalization of the diallyl derivative and double Friedel−Crafts cyclization gave a novel preussomerin analogue which possessed the full carbon skeleton of the natural products.

This paper deals with a fault detection method taking model uncertainties into account. Only sensor imperfections are considered; moreover every uncertain parameter is represented by a bounded variable. By this membership approach, a model represents a set of feasible behaviours; therefore, interval analysis is used to build consistency tests based on the parity space approach. A fault is detected when the residual vector is outside its domain of feasible values. Unfortunately, the dependencies between bounded variables make the previous domain too complex to be exactly evaluated. The proposed solution leads to an overestimation obtained by using a paving algorithm.

This paper presents a Fault Detection and Isolation (FDI) method based on a fuzzy evaluation of residuals. The advantages of this non-Boolean evaluation are described. The ability of this approach to integrate a persistence index of the residual deviations and the sensitivity of the residuals with regard to faults is particularly studied. Some results issued from the implementation of the proposed approach on an urban water supply network illustrate its pertinence.

Recently, fault detection and process monitoring using principal component analysis (PCA) was studied intensively and largely applied to industrial process. This paper proposes an approach to sensor fault detection and isolation via principal component analysis and its application for sensor failure detection of air quality monitoring network in lorraine, France. The method suggested by Dunia et al. (1996) is sensitive to model errors. For this reason, a test on the last principal components is proposed for the detection and the localisation of sensor failure.

The aim of this paper is to establish analytical equations for the problem of finding optimum real poles of the generalized orthonormal basis (GOB) for the class of discrete time systems which unit sample response is of the form h(k)=∑i=1PAibik, where Ai and -1

The control of a ball-mill-hydrocyclone loop is proposed. After presenting the different models used: wet ball-mill, sump and hydrocyclone, a particular control mode is given and its reactions against disturbances are presented. Different transient cases are indicated.

In this paper, an extended model of induction machines bearing short-circuits on a single stator phase is proposed. It is shown that a model based on five differential equations whose state vector contains only stator variables expressed in the stator Park base is more relevant to represent such failures. Moreover a simpler model, valid for small short-circuits, is obtained by linearization. It is further extended in order to take into account resistor disturbances. Finally, a structural analysis is developed to point out; that the stator currents are sufficient to generate residuals for discriminating failures under abnonnal operation.

In this paper we propose an observer for a class of non-linear systems. We consider the stability of the observer where the non-linearities are bounded. This paper gives also the link between the bound of the modelling errors and the dynamic of the observer.

A diagnosis strategy based on fuzzy reasoning is presented in this paper. The first stage of the suggested approach consists in generating residuals revealing of the process functioning state. Two methods of residuals generation for linear-dynamic systems, based on the exploitation of analytical redundancy, are proposed. Then, a procedure of fault isolation using these residuals and based on fuzzy concepts is explained and implemented. To illustrate and show the validity of this approach, it's applied to experimental signals collected on a urban water supply network.

This note is a comment on the paper of B. C. Moon and Z. Bien (Failure detection, identification and accommodation based on a new accommodation filter) 1991, Int. J. Systems Sci., 22, 1025. The authors give an original formulation of the problem of failure detection of sensors and actuators based on a state reconstruction via observers. In this response, we follow the proposed method which is simplified and compared to a more direct approach.

This article describes a method of on-line control of the size of output particles in a ball mill and classifier unit. This is not achieved through direct measuring of particle-size but through action on pulp flow and density.

The mechanism of mass-balance stabilization for a bank of flotation cells is examined. In view of the particular equations involved in seeking to solve the problem by means of a general algorithm, the present paper replaces this with a rank-order resolving technique. By stating a certain hypothesis regarding the distribution of measurement errors, the method proposed gives both the expression of the estimator and its degree of semi-activity to measurement variations together with its variance, which is in effect the primary interest of the present study. A numerical multidimensional example which represents a bank of flotation cells is given in the final section.

As a result of the various discrepancies that affect control systems equipping a process with an adaptative control generally requires a recursive optimal filter to be designed which is capable of producing the estimators for the parameters together with those for the model variables at one and the same time.

This paper deals with the estimation of linear time invariant (LTI) systems using Laguerrien models. The Laguerre functions used in this paper are presented in their general form, i.e. using two exponential factors. The main contribution of this paper is the development of an algorithm which performs the decomposition of a signal into Laguerre series for negative as well as positive time domains. These two decompositions are then aggregated to form a unique bilateral expansion. The method is then used to decompose input and output signals defined for positive as well as negative time (the case of non zero initial conditions of the state process is considered) into their bilateral Laguerre expansions in order to compute the parameters of the transfer function of the system that needs to be identified.

This paper deals with reconciling measurements characterized by an unknown variance from processes described by linear equations. We use the technique of maximum likelihood to give an estimate of the real values and of the measurement errors variance. To make this estimation we assume that the measurement errors follow a normal probability density function with a zero mean and a constant variance. We have developed a relaxation algorithm based on direct iteration to solve the problem of the optimisation of the likelihood function under linear constraints. In the first instance we apply this algorithm to systems operating near a single steady state regime, then we extend the results obtained to the case of several steady state regimes.

Acquisition of coherent and representative data about the state of processes remains a major problem of process control. This paper proposes an approach that ensures coherence in measurements on an operational industrial process. The basic technique employed relies on balance equilibration of resource mass flow. An interesting particularity of the set up is the ease with which it can be adapted to check productivity or detect sensor failures. In the case of sensor failure, it suffices to examine the evaluations before and after balance equilibration. The algorithm was tested by means of computer simulation using data from an industrial metallurgical flowsheet.

The present study deals with the designing and construction of a particle size analyser enabling its User to measure the size of particles belonging to a monophasic pulp.

This paper is devoted to data reconciliation by mass balance equilibration of systems described by bilinear equations. It proposes a new approach for their resolution. We particularly insist on a decomposition algorithm which enables the problem of unmeasured variables to be treated. It allows the classification of variables into observable and unobservable, redundant and non redundant, and gives the relations necessary for their determination. Finally, the results of this algorithm are applied to the mass balance equilibration with the use of an iterative method based on hierarchical calculus.

Generally methods of data reconciliation assume that measurement errors are random gausslan variables with known covariance matrix. In this paper, a new algorithm is proposed for estimating the covariance matrix of measurement errors which is based on the constraint residuals calculated from available data and a linear steady state model. It Is assumed that the measurement errors are random, uncorrelated and have zero mean. The method is based on the maximum likelihood estimator and gives the data reconciliation in addition to the covariance matrix estimation. A computation procedure for the covariance matrix is given and simulation examples are presented.

The robust stability of systems with structured uncertain parameters is investigated. Using the generalized Lyapunov method, the present study shows how the domain of tolerable uncertain parameters for which the system stability is guaranteed can be determined.

An original method for the design of the Luenberger observer is developed for a linear time-invariant system subject to unknown inputs. The paper mainly discusses the problem of determining the matrices of the observer

In this paper, we propose two methods to detect and to localize instrument or actuator failures in linear dynamic systems. These methods are based on analytical redundancy and uses a simultaneous states and inputs estimation. Using the concept of standardized least square residuals, residuals can be generated in a number of ways and the design of fault detection and fault isolation scheme can be presented. The performances of the proposed methods are illustrated on a real process: an inverted pendulum.

In the case of a non-stationary system whose the structure is described by jumping parameters, the residuals found from the difference between measured outputs and inputs model are affected by the variation of those parameters. Therefore, it becomes difficult to distinguish a sensor fault from an actuator fault or a perturbation due to parameters jumping. To bypass this difficulty, an adaptive algorithm has been developed and allows us to scan the fluctuation of model parameters. This algorithm can also give robust residuals for parameters jumping.

An intramolecular hydroxy epoxide opening was used to access the cyclopenta[b]benzofuran ring system of the natural product rocaglaol (2). Our route allowed the stereocontrolled preparation of the rocaglaol derivative (±)-(1S*,3S*,3aR*,8bS*)-3b. The synthesis of the (±)-(3R*)-epimer of 3b was also achieved. Our strategy is well-suited for the production of analogues with variation of the western ring.

The modelling of signals with Laguerre functions is considered. The main contribution concerns the possibility of handling signals defined both in the negative and the positive time domains; this has a potential application to the identification of processes involving inputs and outputs whose knowledge is available near the time origin or for noncausal systems.