Fouling Detection in Heat Exchangers

This paper deals with the design of a nonlinear observer for the purpose of detecting the fouling phenomenon that commonly occurs in heat exchangers. First, the general model of the heat exchanger is presented in terms of partial differential equations. Next, a simplified lump model is derived that is suitable for the observer design. The observer gains are generated by using appropriate Lyapunov functions, equations and inequalities

Robust Neural Network RISE Observer Based Fault Diagnostics And Prediction

A novel fault diagnostics and prediction scheme in continuous time is introduced for a class of nonlinear systems. The proposed method uses a novel neural network (NN) based robust integral sign of the error (RISE) observer, or estimator, allowing for semi-global asymptotic stability in the presence of NN approximation errors, disturbances and unmodeled dynamics. This is in comparison to typical results presented in the literature that show only boundedness in the presence of uncertainties. The output of the observer/estimator is compared with that of the nonlinear system and a residual is used for declaring the presence of a fault when the residual exceeds a user defined threshold. The NN weights are tuned online with no offline tuning phase. The output of the RISE observer is utilized for diagnostics. Additionally, a method for time-to-failure (TTF) prediction, a first step in prognostics, is developed by projecting the developed parameter-update law under the assumption that the nonlinear system satisfies a linear-in-the-parameters (LIP) assumption. The TTF method uses known critical values of a system to predict when an estimated parameter will reach a known failure threshold. The performance of the NN/RISE observer system is evaluated on a nonlinear system and a simply supported beam finite element analysis (FEA) simulation based on laboratory experiments. Results show that the proposed method provides as much as 25% increased accuracy while the TTF scheme renders a more accurate prediction. © 2010 IEEE

A Geometric Approach to Fault Detection and Isolation of Continuous-Time Markovian Jump Linear Systems

This paper is concerned with development of novel fault detection and isolation (FDI) strategies for the Markovian jump linear systems (MJLS's) and the MJLS's with time-delays (MJLSD's). First a geometric property that is related to the unobservable subspace of MJLS's is presented. The notion of a finite unobservable subspace is then introduced for the MJLSD's. The concept of unobservability subspace is introduced for both the MJLS's and the MJLSD's and an algorithm for its construction is described. The necessary and sufficient conditions for solvability of the fundamental problem of residual generation (FPRG) for the MJLS's are developed by utilizing our introduced unobservability subspace. Furthermore, sufficient solvability conditions of the FPRG for the MJLSD's are also derived. Finally, sufficient conditions for designing an H∞-based FDI algorithm for the MJLS's with an unknown transition matrix that are also subject to input and output disturbances are developed

Fault detection of multivariable system using its directional properties

A novel algorithm for making the combination of outputs in the output zero direction of the plant always equal to zero was formulated. Using this algorithm and the result of MacFarlane and Karcanias, a fault detection scheme was proposed which utilizes the directional property of the multivariable linear system. The fault detection scheme is applicable to linear multivariable systems. Results were obtained for both continuous and discrete linear multivariable systems. A quadruple tank system was used to illustrate the results. The results were further verified by the steady state analysis of the plant

A control-theoretical fault prognostics and accommodation framework for a class of nonlinear discrete-time systems

Fault diagnostics and prognostics schemes (FDP) are necessary for complex industrial systems to prevent unscheduled downtime resulting from component failures. Existing schemes in continuous-time are useful for diagnosing complex industrial systems and no work has been done for prognostics. Therefore, in this dissertation, a systematic design methodology for model-based fault prognostics and accommodation is undertaken for a class of nonlinear discrete-time systems. This design methodology, which does not require any failure data, is introduced in six papers. In Paper I, a fault detection and prediction (FDP) scheme is developed for a class of nonlinear system with state faults by assuming that all the states are measurable. A novel estimator is utilized for detecting a fault. Upon detection, an online approximator in discrete-time (OLAD) and a robust adaptive term are activated online in the estimator wherein the OLAD learns the unknown fault dynamics while the robust adaptive term ensures asymptotic performance guarantee. A novel update law is proposed for tuning the OLAD parameters. Additionally, by using the parameter update law, time to reach an a priori selected failure threshold is derived for prognostics. Subsequently, the FDP scheme is used to estimate the states and detect faults in nonlinear input-output systems in Paper II and to nonlinear discrete-time systems with both state and sensor faults in Paper III. Upon detection, a novel fault isolation estimator is used to identify the faults in Paper IV. It was shown that certain faults can be accommodated via controller reconfiguration in Paper V. Finally, the performance of the FDP framework is demonstrated via Lyapunov stability analysis and experimentally on the Caterpillar hydraulics test-bed in Paper VI by using an artificial immune system as an OLAD --Abstract, page iv

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

Diagnostic à base de modèles non linéaires. : Application au circuit carburant d'une turbomachine

The current gas turbine regulation systems are based on complex architectures that manufacturers tend to make more modular with more cost effective technologies while ensuring a greater or equal level of reliability. In this context, the fuel system health monitoring, which aims to identify critical hydraulic components dysfunction, allows to reduce maintenance costs, to improve maintainability level and to ensure gas turbine availability. The present study focuses on the development of performant and robust diagnosis methods for the detection and isolation of faults affecting primary fuel system hydraulic functions. Existing nonlinear model based residual generation methods are presented and applied to the fuel system. The analytical approach for decoupling, combined with extended Kalman filters, helps fault isolation by generating residual structures. A new approach based on differential flatness theory is proposed for nonlinear systems fault diagnosis with an application to the fuel system. Sliding mode differentiators are used to estimate derived signals that are necessary for the application of some residual generation methods. Numerical simulations illustrate the efficiency of obtained results. An experimental application is presented using a real data set from a partial test bench provided by Turbomeca company of the SAFRAN group.Les systèmes de régulation des turbomoteurs actuels sont basés sur des architectures complexes que les constructeurs tendent à rendre plus modulaires avec des technologies plus économiques tout en garantissant un niveau de fiabilité supérieur ou égal. Dans ce contexte, la surveillance du circuit carburant, qui a pour but de déceler les dysfonctionnements des composants hydrauliques critiques, permet de réduire le coût de maintenance, d'améliorer le niveau de maintenabilité et d'assurer la disponibilité des turbomoteurs. La présente étude porte sur l'élaboration de méthodes de diagnostic performantes et robustes permettant la détection et la localisation des défauts impactant les fonctions hydrauliques primaires du circuit carburant. Des méthodes existantes de génération de résidus à base de modèles non linéaires sont présentées et appliquées au cas du circuit carburant. L'approche analytique pour le découplage, combinée avec des filtres de Kalman étendus, permet la structuration des résidus pour assurer la localisation des défauts. Une nouvelle approche basée sur la théorie de platitude différentielle est proposée pour le diagnostic de défauts des systèmes non linéaires avec une application au cas du circuit carburant. Les différentiateurs à modes glissants sont utilisés pour l'estimation des dérivées de signaux nécessaires à l'application de certaines méthodes de génération de résidus. Des simulations numériques illustrent la pertinence des résultats obtenus. Une application expérimentale est présentée en utilisant un jeu de données réelles issues d'un banc d'essais partiel et fournies par la société Turbomeca du groupe SAFRAN

Fault detection and isolation in a networked multi-vehicle unmanned system

Recent years have witnessed a strong interest and intensive research activities in the area of networks of autonomous unmanned vehicles such as spacecraft formation flight, unmanned aerial vehicles, autonomous underwater vehicles, automated highway systems and multiple mobile robots. The envisaged networked architecture can provide surpassing performance capabilities and enhanced reliability; however, it requires extending the traditional theories of control, estimation and Fault Detection and Isolation (FDI). One of the many challenges for these systems is development of autonomous cooperative control which can maintain the group behavior and mission performance in the presence of undesirable events such as failures in the vehicles. In order to achieve this goal, the team should have the capability to detect and isolate vehicles faults and reconfigure the cooperative control algorithms to compensate for them. This dissertation deals with the design and development of fault detection and isolation algorithms for a network of unmanned vehicles. Addressing this problem is the main step towards the design of autonomous fault tolerant cooperative control of network of unmanned systems. We first formulate the FDI problem by considering ideal communication channels among the vehicles and solve this problem corresponding to three different architectures, namely centralized, decentralized, and semi-decentralized. The necessary and sufficient solvability conditions for each architecture are also derived based on geometric FDI approach. The effects of large environmental disturbances are subsequently taken into account in the design of FDI algorithms and robust hybrid FDI schemes for both linear and nonlinear systems are developed. Our proposed robust FDI algorithms are applied to a network of unmanned vehicles as well as Almost-Lighter-Than-Air-Vehicle (ALTAV). The effects of communication channels on fault detection and isolation performance are then investigated. A packet erasure channel model is considered for incorporating stochastic packet dropout of communication channels. Combining vehicle dynamics and communication links yields a discrete-time Markovian Jump System (MJS) mathematical model representation. This motivates development of a geometric FDI framework for both discrete-time and continuous-time Markovian jump systems. Our proposed FDI algorithm is then applied to a formation flight of satellites and a Vertical Take-Off and Landing (VTOL) helicopter problem. Finally, we investigate the problem of fault detection and isolation for time-delay systems as well as linear impulsive systems. The main motivation behind considering these two problems is that our developed geometric framework for Markovian jump systems can readily be applied to other class of systems. Broad classes of time-delay systems, namely, retarded, neutral, distributed and stochastic time-delay systems are investigated in this dissertation and a robust FDI algorithm is developed for each class of these systems. Moreover, it is shown that our proposed FDI algorithms for retarded and stochastic time-delay systems can potentially be applied in an integrated design of FDI/controller for a network of unmanned vehicles. Necessary and sufficient conditions for solvability of the fundamental problem of residual generation for linear impulsive systems are derived to conclude this dissertation

A geometric approach to fault detection and isolation for bilinear systems: application to heat exchangers

info:eu-repo/semantics/publishe