Search

The paper investigates the development of an active fault tolerant control strategy obtained from the integration of a fault detection and diagnosis module with a controller reconfiguration method. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via a nonlinear approach. The controller reconfiguration mechanism exploited in the control loop relies on the on–line estimate of the fault signal. The active fault tolerant control scheme is applied to a simulator of an aerial vehicle in several flight conditions, in the presence of actuator faults, turbulence, measurement noise, and modelling errors.

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.

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.

This paper addresses the problem of the diagnosis of the input sensor faults on general aviation aircraft, characterised by a non–linear model, in the presence of windgust disturbance and measurement errors. In particular, this work investigates the design of residual generators in order to realise complete diagnosis schemes when additive faults are present. The use of an input–output description for the linearised model of the aircraft allows to compute in a straightforward way the residual generators for fault diagnosis. These tools lead to dynamic filters that can achieve both good disturbance signal decoupling and robustness properties with respect to both linearisation error and measurement noise. Mathematical descriptions of theaircraftmeasurementsensorsarealsotakenintoaccount. The results obtained in the simulation of the faulty behaviour of a PIPER PA30 aircraft are finally reported.

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.

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.

In this paper consideration is given to the problem of determining an optimal estimate of the output of a linear dynamic SISO system from the knowledge of the input-output data corrupted by additive noise. The solution of this problem can be divided into two steps: first. the model of the system and of the noise affecting the data is identified. then a Kalman filter is designed on the basis of this model. Since the result of the identification scheme may be a whole family of models. a comparison between these different systems is analyzed with refcrence to the behavior of the associated Kalman filters. Some silmulation results are finally discussed.

The problem of determining linear models from data corrupted by noise has been revisited by several authors in recent years. With reference to the Frisch scheme, this work presents some geometrical aspects regarding the sets of solutions obtained in the identification procedure. An analysis is made on how these sets vary as the amount of noise on the data varies; the existence of particular solutions for data linked by a number of linear relations greater than one, is also shown.

This paper studies the application results of a method for estimating and tuning the parameters of a digital PID controller. The PID coefficients are computed by means of the identification of the parameters of a discrete-time time invariant input-output dynamic linear model of the process under investigation. The proportional, integral and derivative PID controller terms are thus evaluated by solving a set of algebraic equations obtained by imposing a number of constraints regarding the step response dynamic characteristics of the monitored system. The achieved results are shown when the proposed tuning approach is applied to the real data acquired from a low power boiler used to generate hot water for both domestic use and heating. Therefore, the reliability properties of the PID designed according to the suggested technique are verified by considering different working conditions of the boiler plant. Moreover, these extensive experiments are also used for assessing the overall capabilities of the developed PID design scheme, in the presence of both measurement and modelling errors. Comparisons with different controller design methods available in the related literature are finally reported.

This paper describes the project regarding the problem of the detection and isolation of the input and output sensor faults on a general aviation aircraft, characterised by a non–linear model, in the presence of wind gust disturbance and measurement errors. In particular, this work investigates the design of residual generators in order to realise complete diagnosis schemes when additive faults are present. The use of an input–output description for the linearised model of the aircraft allows to compute in a straightforward way the residual generators for fault detection and isolation. These tools lead to dynamic filters that can achieve both good disturbance signal de–coupling and robustness properties with respect to both linearisation error and measurement noise. Mathematical descriptions of the aircraft measurement sensors are also taken into account. The results obtained in the simulation of the faulty behaviour of a PIPER PA30 aircraft are finally reported

This paper addresses the identification of non-linear dynamic 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 structure through input-output data acquired from a dynamic process. 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.

The fuzzy model identification problem from noisy data is addressed. The piecewise linear fuzzy model structure is used as a nonlinear prototype for a multi-input, single-output unknown system. The consequent of the fuzzy model is identified using noisy data, e.g. collected from experiments on a real system. The identification procedure is formulated within the Frisch scheme, well established for linear systems, which has been modified and improved to be applied in fuzzy systems field.

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.

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.

Background TH-302 is a hypoxia targeted prodrug with a hypoxia-triggered 2-nitroimidazole component designed to release the DNA alkylator, bromo-isophosphoramide mustard (Br-IPM), when reduced in severe hypoxia. A randomized Phase 2B study (NCT01144455) was conducted to assess the benefit of G + T to standard dose G as first-line therapy of PAC. Materials and methods An open-label multi-center study of two dose levels of TH-302 (240 mg/m2 or 340 mg/m2) in combination with G versus G alone (randomized 1:1:1). G (1000 mg/m2) and T were administered IV over 30-60 minutes on Days 1, 8 and 15 of a 28-day cycle. Patients on the G could crossover after progression and be randomized to a G + T arm. The primary efficacy endpoint was a comparison of progression-free survival (PFS) between the combination arms and G alone (80% power to detect 50% improvement in PFS with one-sided alpha of 10%). Summary PFS outcome has previously been reported; more detailed PFS as well as the initial overall survival (OS) data are presented. Results 214 pts were treated; 164 (77%) Stage IV and 50 (23%) Stage IIIB. Median age 65 (range 29-86); 126 M/88 F; 40% ECOG 0/60% ECOG 1. Receiving 6 or more cycles: 32% G; 45% G + T240; 55% G + T340. Median PFS was 3.6 mo in G vs 5.5 mo in G + T240 (p = 0.031) and 6.0 mo in G + T340 (p = 0.008). Poorer prognostic factors (older age, poorer performance status, reduced albumin) were associated with larger treatment effect. Median OS was 7.0 mo in G vs 9.0 in G + T240 and 9.5 mo in G + T340. RECIST best response was 12% in G vs 17% in G + T240 and 27% in G + T340. CA19-9 decreases were significantly greater G + T340. A >50% CA19-9 decrease was 52% with G vs 50% with G + T240 and 70% with G + T340. AEs leading to discontinuation were: 16% G, 15% G + T240 and 11% G + T340. Rash (45% in G + T340) and stomatitis (36% in G + T340) were greater in combination, 4 pts Grade 3 rash. Grd 3/4 thrombocytopenia were 11% G, 39% G + T240 and 59% G + T340 and Grd 3/4 neutropenia were 28% G, 56% G + T240 and 59% G + T340. Conclusions The combination of G plus TH-302 improved the efficacy of G. A TH-302 dose of 340 mg2 was identified for future studies. Skin and mucosal toxicity and myelosuppression were the most common TH-302 related AEs with no increase in treatment discontinuation. Disclosure All authors have declared no conflicts of interest.

An important problem to be solved before trying to model such complex phenomena as eutrophication is the selection of model inputs. The relevance of this step is due to the necessity of obtaining models whose complexity compares favourably with the number of samples in the sequences used for their determination; moreover, a knowledge of the main factors affecting the evolution of eutrophication is, per se, very important. Most approaches described in the literature rely on assumptions which are made a priori or on the results of algebraic correlation analyses performed on the data (...)

The purpose of this work is to show models obtained by identification procedures can be used to predict, with satisfactory accuracy, the dynamic behavior of polymer waterflooding processes. The usual approach to forecast the behavior of these processes relies on modeling techniques to obtain compositional models that are intrinsically realistic but difficult to use.

The structural identification of linear muitivariabie systems can be performed on the basis of the predicted percentage of reconstruction errors (PPCRE) associated to a sequence of increasing-complexity models. This paper extends some previous results describing the properties of the PPCRE for canonical models whose parameters are estimated by means of the eigenvector method.

A new canonical form for input-output difference (differential) descriptions for linear multivariable systems is proposed. A complete algorithm for the transformation of generical input-output descriptions to the canonical form is also described. This algorithm is based on elementary row operations performed on polynomial matrices.

In this paper a class of input-output multistructural (overlapping) models is developed starting from the usual state-space ones. The algebraical link between state-space and input-output multistructural models is then investigated. The problems arising in the use of input-output multistructural models in the identification of multivariable systems are finally discussed and a procedure based on the use of these models proposed.

Some general properties of recently introduced inputs-output multistructural (overlapping) models are analysed and compared with the properties of the well-known input-output canonical forms. Algorithms for the transformation of a generic input-output model to a multi-structural one and for transformation between ‘ adjacent’ multistructural models are then proposed. The use of the models in on-line multivariate identification is also discussed and a numerical example given.

The identification of complex industrial processes on the only basis of input-output sequences obtained during normal operating conditions plays, at present, an important role-A distillation column, for instance, can work in many different conditions (feed flow, composition, temperature etc.) also if it has been designed wit|h reference to a standard situation. This requires the presence of a controller based on an accurate previsional model of the process. This paper describes the construction of a mathematical model for a ethane-ethylene distillation column by identification techniques .

A simulation procedure for the computation of current wave forms in a system composed by a three-phase in verter driving an induction motor is proposed.The amplitude of the fundamental harmonic of ...

It is well known that the dynamics of a system depends not only on pole configuration but also on the particuiar choice of the feedback matrix assigning the desired poles to the given plant. The aim of this paper is to introduce both a method for pole assignment and a criterion for classifying the systems having the same pole configuration and obtained by such a method.

A simple generalization of the Luenberger method for pole assignment is introduced which allows one to obtain a greater number of feedback laws giving both the desired poles to the system and the choice of a larger number of solutions to the designer.

Some classical schemes in algebraic system identification are first recalled and compared. It is shown that, in most cases, the solution is obtained thanks to additional assumptions which are not deducible from the available data. The identification problem for linear dynamic systems is then solved on the basis of the Frisch scheme, in order to obtain the whole set of models compatible with noisy input-output sequences. The main result here proposed concerns the unicity of the solution when the data are affected by additive white noise.

A promising approach regarding variable-speed fractional horse power drives consists in the use of a capacitor-run motor driven by D-modulation PWM inverters. A drive of this kind is not only cheap and reliable but allows also an easy implementation of the required linear relationship between the drive voltage frequency and amplitude. This paper proposes a simulation procedure for such systems that overcomes the problems posed by the complex shape of the resulting drive voltage.

Ordered sets of Kronecker invariants for linear multivariable systems are often considered in the literature to solve analysis and synthesis problems. This paper shows how to obtain ordered sets of Kronecker invariants by means of simple permutations in the input (output) vector components.

This paper regards the use of system theory techniques in the development of models describing the phosphorus kinetics in fresh waters with the objective to understand the observed kinetics better in order to obtain indications on the design of future field experiments. Identification techniques have been applied to the data regarding 49 experiments on phosphorus cycling in Kootenay Lake, British Columbia, Canada, in order to determine the minimal number of compartments required to model the phosphorus dynamical behaviour. Canonical state-space models have then been identified both from raw and smoothed data. The parameters in these models do not have a direct physical meaning; they can, however, be identified from the measured data and the obtained models describe with good accuracy the considered kinetics. The identification of physical models has also been performed; in this case, however, only two compartments have been considered according to the results of the identifiability analysis of the complete model parameters. Results give further insight on the phosphorus processes and show that the complete phosphorus cycle through the four compartments described by Lean can be identified only when phosphorus cycles rapidly through the system.

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.

This paper presents an identification method for errors-in-variables systems with input-output measurements affected by white and mutually correlated noises. The procedure, based on a frequency-domain approach, allows to uniquely determine both the characteristics of the noises affecting the data and the transfer function of the process under investigation. A numerical example is reported in order to illustrate the suggested technique and to verify its numerical implementation.

A nonlinear dynamic process can be described as a composition of several local affine models selected according to the process operating conditions. Such a compound system requires the identification of the local models from data. This work addresses a method for the identification and the optimal selection of the local affine models from a sequence of noisy measurements acquired from the process. The developed technique is applied to the identification of a simulated model.

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.

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.

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.

This paper addresses the problem of the detection and isolation of actuator faults on a general aviation aircraft, characterised by a nonlinear model, in the presence of wind gust disturbances. In particular, this work investigates the design of residual generators in order to realise complete diagnosis schemes when additive faults are present. The use of a canonical input-output polynomial description for the linearised model of the aircraft allows to compute in a straightforward way minimal order residual generators. These tools lead to dynamic filters that can guarantee both disturbance signal decoupling and robustness properties with respect to linearisation errors. The results obtained in the simulation of the faulty behaviour of a Piper PA30 are finally reported.