Process operating mode monitoring : switching online the right controller

This paper presents a structure which deals with process operating mode monitoring and allows the control law reconfiguration by switching online the right controller. After a short review of the advances in switching based control systems during the last decade, we introduce our approach based on the definition of operating modes of a plant. The control reconfiguration strategy is achieved by online selection of an adequate controller, in a case of active accommodation. The main contribution lies in settling up the design steps of the multicontroller structure and its accurate integration in the operating mode detection and accommodation loop. Simulation results show the effectiveness of the operating mode detection and accommodation (OMDA) structure for which the design steps propose a method to study the asymptotic stability, switching performances improvement, and the tuning of the multimodel based detector

Fault Diagnosis Techniques for Linear Sampled Data Systems and a Class of Nonlinear Systems

This thesis deals with the fault diagnosis design problem both for dynamical continuous time systems whose output signal are affected by fixed point quantization,\ud referred as sampled-data systems, and for two different applications whose dynamics are inherent high nonlinear: a remotely operated underwater vehicle and a scramjet-powered hypersonic vehicle.\ud Robustness is a crucial issue. In sampled-data systems, full decoupling of disturbance terms from faulty signals becomes more difficult after discretization.\ud In nonlinear processes, due to hard nonlinearity or the inefficiency of linearization, the “classical” linear fault detection and isolation and fault tolerant control methods may not be applied.\ud Some observer-based fault detection and fault tolerant control techniques are studied throughout the thesis, and the effectiveness of such methods are validated with simulations. The most challenging trade-off is to increase sensitivity to faults and robustness to other unknown inputs, like disturbances. Broadly speaking, fault detection filters are designed in order to generate analytical diagnosis functions, called residuals, which should be independent with respect to the system operating state and should be decoupled from disturbances. Decisions on the occurrence of a possible fault are therefore taken on the basis such residual signals

Fault Detection, Diagnosis and Fault Tolerance Approaches in Dynamic Systems based on Black-Box Models

In this dissertation new contributions to the research area of fault detection and diagnosis in dynamic systems are presented. The main research effort has been done on the development of new on-line model-based fault detection and diagnosis (FDD) approaches based on blackbox models (linear ARX models, and neural nonlinear ARX models). From a theoretical point of view a white-box model is more desirable to perform the FDD tasks, but in most cases it is very hard, or even impossible, to obtain. When the systems are complex, or difficult to model, modelling based on black-box models is usually a good and often the only alternative. The performance of the system identification methods plays a crucial role in the FDD methods proposed. Great research efforts have been made on the development of linear and nonlinear FDD approaches to detect and diagnose multiplicative (parametric) faults, since most of the past research work has been done focused on additive faults on sensors and actuators. The main pre-requisites for the FDD methods developed are: a) the on-line application in a real-time environment for systems under closed-loop control; b) the algorithms must be implemented in discrete time, and the plants are systems in continuous time; c) a two or three dimensional space for visualization and interpretation of the fault symptoms. An engineering and pragmatic view of FDD approaches has been followed, and some new theoretical contributions are presented in this dissertation. The fault tolerance problem and the fault tolerant control (FTC) have been investigated, and some ideas of the new FDD approaches have been incorporated in the FTC context. One of the main ideas underlying the research done in this work is to detect and diagnose faults occurring in continuous time systems via the analysis of the effect on the parameters of the discrete time black-box ARX models or associated features. In the FDD methods proposed, models for nominal operation and models for each faulty situation are constructed in off-line operation, and used a posteriori in on-line operation. The state of the art and some background concepts used for the research come from many scientific areas. The main concepts related to data mining, multivariate statistics (principal component analysis, PCA), linear and nonlinear dynamic systems, black-box models, system identification, fault detection and diagnosis (FDD), pattern recognition and discriminant analysis, and fault tolerant control (FTC), are briefly described. A sliding window version of the principal components regression algorithm, termed SW-PCR, is proposed for parameter estimation. The sliding window parameter estimation algorithms are most appropriate for fault detection and diagnosis than the recursive algorithms. For linear SISO systems, a new fault detection and diagnosis approach based on dynamic features (static gain and bandwidth) of ARX models is proposed, using a pattern classification approach based on neural nonlinear discriminant analysis (NNLDA). A new approach for fault detection (FDE) is proposed based on the application of the PCA method to the parameter space of ARX models; this allows a dimensional reduction, and the definition of thresholds based on multivariate statistics. This FDE method has been combined with a fault diagnosis (FDG) method based on an influence matrix (IMX). This combined FDD method (PCA & IMX) is suitable to deal with SISO or MIMO linear systems. Most of the research on the fault detection and diagnosis area has been done for linear systems. Few investigations exist in the FDD approaches for nonlinear systems. In this work, two new nonlinear approaches to FDD are proposed that are appropriate to SISO or MISO systems. A new architecture for a neural recurrent output predictor (NROP) is proposed, incorporating an embedded neural parallel model, an external feedback and an adjustable gain (design parameter). A new fault detection and diagnosis (FDD) approach for nonlinear systems is proposed based on a bank of neural recurrent output predictors (NROPs). Each neural NROP predictor is tuned to a specific fault. Also, a new FDD method based on the application of neural nonlinear PCA to ARX model parameters is proposed, combined with a pattern classification approach based on neural nonlinear discriminant analysis. In order to evaluate the performance of the proposed FDD methodologies, many experiments have been done using simulation models and a real setup. All the algorithms have been developed in discrete time, except the process models. The process models considered for the validation and tests of the FDD approaches are: a) a first order linear SISO system; b) a second order SISO model of a DC motor; c) a MIMO system model, the three-tank benchmark. A real nonlinear DC motor setup has also been used. A fault tolerant control (FTC) approach has been proposed to solve the typical reconfiguration problem formulated for the three-tank benchmark. This FTC approach incorporates the FDD method based on a bank of NROP predictors, and on an adaptive optimal linear quadratic Gaussian controller

Fault Diagnosis and Performance Recovery Based on the Dynamic Safety Margin

The complexity of modern industrial processes makes high dependability an essential demand for reducing production loss, avoiding equipment damage, and increasing human safety. A more dependable system is a system that has the ability to: 1) detect faults as fast as possible; 2) diagnose them accurately; 3) recover the system to the nominal performance as much as possible. Therefore, a robust Fault Detection and Isolation (FDI) and a Fault Tolerant Control (FTC) system design have attained increased attention during the last decades. This thesis focuses on the design of a robust model-based FDI system and a performance recovery controller based on a new performance index called Dynamic Safety Margin (DSM). The DSM index is used to measure the distance between a predefined safety boundary in the state space and the system state trajectory as it evolves. The DSM concept, its computation methods, and its relationship to the state constraints are addressed. The DSM can be used in different control system applications; some of them are highlighted in this work. Controller design based on DSM is especially useful for safety-critical systems to maintain a predefined margin of safety during the transient and in the presence of large disturbances. As a result, the application of DSM to controller design and adaptation is discussed in particular for model predictive control (MPC) and PID controller. Moreover, an FDI scheme based on the analysis of the DSM is proposed. Since it is difficult to isolate different types of faults using a single model, a multi-model approach is employed in this FDI scheme. The proposed FDI scheme is not restricted to a special type of fault. In some faulty situations, recovering the system performance to the nominal one cannot be fulfilled. As a result, reducing the output performance is necessary in order to increase the system availability. A framework of FTC system is proposed that combines the proposed FDI and the controllers design based on DSM, in particular MPC, with accepted degraded performance in order to generate a reliable FTC system. The DSM concept and its applications are illustrated using simulation examples. Finally, these applications are implemented in real-time for an experimental two-tank system. The results demonstrate the fruitfulness of the introduced approaches

Fractional Order Fault Tolerant Control - A Survey

In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies