Observer-based Fault Diagnosis: Applications to Exothermic Continuous Stirred Tank Reactors

For chemical engineering dynamic systems, there is an increasing demand for better process performance, high product quality, absolute reliability & safety, maximum cost efficiency and less environmental impact. Improved individual process components and advanced automatic control techniques have brought significant benefits to the chemical industry. However, fault-free operation of processes cannot be guaranteed. Timely fault diagnosis and proper management can help to avoid or at least minimize the undesirable consequences. There are many techniques for fault diagnosis, and observer-based methods have been widely studied and have proved to be efficient for fault diagnosis. The basic idea of an observer-based approach is to generate a specific residual signal which carries the information of specific faults, as well as the information of process disturbances, model uncertainties, other faults and measurement noises. For fault diagnosis, the residual should be sensitive to faults and insensitive to other unknown inputs. With this feature, faults can be easily detected and may be isolated and identified. This thesis applied an observer-based fault diagnosis method to three exothermic CSTR case studies. In order to improve the operational safety of exothermic CSTRs with risks of runaway reactions and explosion, fault diagnostic observers are built for fault detection, isolation and identification. For this purpose, different types of most common faults have been studied in different reaction systems. For each fault, a specific observer and the corresponding residual is built, which works as an indicator of that fault and is robust to other unknown inputs. For designing linear observers, the original nonlinear system is linearized at steady state, and the observer is designed based on the linearized system. However, in the simulations, the observer is tested on the nonlinear system instead of the linearized system. In addition, an efficient & effective general MATLAB program has been developed for fault diagnosis observer design. Extensive simulation studies have been performed to test the fault diagnostic observer on exothermic CSTRs. The results show that the proposed fault diagnosis scheme can be directly implemented and it works well for diagnosing faults in exothermic chemical reactors

Robust model-based fault diagnosis for chemical process systems

Fault detection and diagnosis have gained central importance in the chemical process industries over the past decade. This is due to several reasons, one of them being that copious amount of data is available from a large number of sensors in process plants. Moreover, since industrial processes operate in closed loop with appropriate output feedback to attain certain performance objectives, instrument faults have a direct effect on the overall performance of the automation system. Extracting essential information about the state of the system and processing the measurements for detecting, discriminating, and identifying abnormal readings are important tasks of a fault diagnosis system. The goal of this dissertation is to develop such fault diagnosis systems, which use limited information about the process model to robustly detect, discriminate, and reconstruct instrumentation faults. Broadly, the proposed method consists of a novel nonlinear state and parameter estimator coupled with a fault detection, discrimination, and reconstruction system. The first part of this dissertation focuses on designing fault diagnosis systems that not only perform fault detection and isolation but also estimate the shape and size of the unknown instrument faults. This notion is extended to nonlinear processes whose structure is known but the parameters of the process are a priori uncertain and bounded. Since the uncertainty in the process model and instrument fault detection interact with each other, a novel two-time scale procedure is adopted to render overall fault diagnosis. Further, some techniques to enhance the convergence properties of the proposed state and parameter estimator are presented. The remaining part of the dissertation extends the proposed model-based fault diagnosis methodology to processes for which first principles modeling is either expensive or infeasible. This is achieved by using an empirical model identification technique called subspace identification for state-space characterization of the process. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor), an industrial melter process, and a debutanizer plant

Model based fault diagnosis and prognosis of class of linear and nonlinear distributed parameter systems modeled by partial differential equations

With the rapid development of modern control systems, a significant number of industrial systems may suffer from component failures. An accurate yet faster fault prognosis and resilience can improve system availability and reduce unscheduled downtime. Therefore, in this dissertation, model-based prognosis and resilience control schemes have been developed for online prediction and accommodation of faults for distributed parameter systems (DPS). First, a novel fault detection, estimation and prediction framework is introduced utilizing a novel observer for a class of linear DPS with bounded disturbance by modeling the DPS as a set of partial differential equations. To relax the state measurability in DPS, filters are introduced to redesign the detection observer. Upon detecting a fault, an adaptive term is activated to estimate the multiplicative fault and a tuning law is derived to tune the fault parameter magnitude. Then based on this estimated fault parameter together with its failure limit, time-to-failure (TTF) is derived for prognosis. A novel fault accommodation scheme is developed to handle actuator and sensor faults with boundary measurements. Next, a fault isolation scheme is presented to differentiate actuator, sensor and state faults with a limited number of measurements for a class of linear and nonlinear DPS. Subsequently, actuator and sensor fault detection and prediction for a class of nonlinear DPS are considered with bounded disturbance by using a Luenberger observer. Finally, a novel resilient control scheme is proposed for nonlinear DPS once an actuator fault is detected by using an additional boundary measurement. In all the above methods, Lyapunov analysis is utilized to show the boundedness of the closed-loop signals during fault detection, prediction and resilience under mild assumptions --Abstract, page iv

Load Disturbance Torque Estimation for Motor Drive Systems with Application to Electric Power Steering System

Motors are widely used in industries due to its ability to provide high mechanical power in speed and torque applications. Its flexibility to control and quick response are other reasons for its widespread use. Disturbance torque acting on the motor shaft is a major factor which affects the motor performance. Considering the load disturbance torque while designing the control for the motor makes the system more robust to load changes. Most disturbance observers are designed for steady state load conditions. The observer designed here considers a general case making no assumptions about the load torque dynamics. The observer design methods to be used under different disturbance conditions are also discussed and the performances compared. The designed observer is tested in a Hardware-in-Loop (HIL) setup for different load conditions. A motor load torque estimation based Fault Tolerant Control (FTC) is then designed for an Electric Power Steering (EPS) system

Subspace based data-driven designs of fault detection systems

The thesis focuses on advanced methods of fault detection and diagnosis suitable for application in large-scale processes. The theory of fault diagnosis mainly comprises development of mathematical models for observing critical changes in the process under consideration. The so-called residual signal is used for the purpose of detecting abnormal events and diagnosing their nature. For large-scale processes, it is difficult to build their models mathematically. Therefore, very often historical data from regular sensor measurements, event-logs and records are used to directly identify relationship between plant's input and output. On these lines, the thesis presents a data-driven design of fault detection systems which reduces the computation burden by identifying only the key components and not the entire process model itself. The novel design method is also studied within the context of parameter varying systems. Since many processes undergo temporary fluctuation of their crucial parameters, which can not be ruled out as faults, the fault detection system must be able to adapt to these changes. This is realized in the thesis with two efficient algorithms, which are based on recursive identification techniques. The theoretical contribution in this thesis also revolves around improvising the novel data-drive design of fault detection systems. In other words, the identification procedure is optimized by reformulating it as “closed-loop” identification or identification of Kalman filter. Also, the algorithm is numerically optimized by using QR based decomposition technique. The thesis also presents application results of different algorithms derived in this work. As benchmarks, the Tennessee Eastman chemical plant and the continuously stirred tank heater are considered. The novel algorithms are compared with the existing popular techniques from the literature.Die Arbeit konzentriert sich auf fortgeschrittene Methoden zur Fehlererkennung und Diagnose für den Einsatz in Mehrgrößen Systemen. Üblicherweise umfasst die Fehlerdiagnose Entwicklung von mathematischen Modellen zur Beobachtung der Veränderungen in den ursprünglichen Prozessen. Dabei wird ein so genanntes Residuensignal zur von Fehlern benutzt, welches im Fehlerfall einen Ausschlag zeigt. Für Mehrgrößen Systeme, ist es im Allgemeinen schwierig, mathematische Modelle zu erstellen, die mathematisch abgeleitet werden können. Deshalb werden Daten aus dem Prozess, z.B. aus regelmäßigen Messungen, Event-Logs oder Records verwendet, um Beziehungen zwischen Prozess-Eingang und Ausgang abzubilden. Davon ausgehend werden in der vorliegenden Arbeit Verfahren entwickelt um ein Datenbasiertes Fehlererkennungssystem zu generieren, welches ohne Modelidentifikation arbeitet. In dieser Arbeit wird das Problem der Datenbasierten Fehlererkennung weiter im Rahmen der so genannten Parameter Varianten Systeme untersucht. Da viele Prozesse vorübergehenden Parameterschwankungen unterliegen, die nicht als Fehler ausgeschlossen werden können, muss das Fehlererkennung System in der Lage sein, die Veränderungen zu adaptieren. Ein solches lernendes Fehlererkennungssystem ist hier an Hand von zwei effizienten Algorithmen und mit rekursiver Identifikation realisiert. Der Beitrag in dieser Arbeit ist auch ein modifiziertes, optimales Subraum Identifikation basiertes Entwurf. Darüber hinaus wird das Identifikationsverfahren auf die Hauptkomponenten beschränkt und das ursprüngliche Problem wird für die optimale Parameterschätzung als „Closed-Loop“ Identifikation oder Identifikation des Kalman Filters umformuliert. Die gesamte Konstruktion ist numerisch über eine QR Zerlegung numerisch optimiert. Die Arbeit stellt auch Ergebnisse der Applikation verschiedener Algorithmen vor. Als Versuchstand wurden das Tennessee Eastman Prozess und eine kontinuierlich gerührte Tankheizung verwendet. Die Algorithmen dieser Arbeit werden mit dem ursprünglichen und anderen Identifikationsverfahren verglichen

FAULT DETECTION AND ISOLATION FOR WIND TURBINE DYNAMIC SYSTEMS

This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier

Monitoring and Fault Diagnosis for Chylla-Haase Polymerization Reactor

The main objective of this research is to develop a fault detection and isolation (FDI) methodologies for Cylla-Haase polymerization reactor, and implement the developed methods to the nonlinear simulation model of the proposed reactor to evaluate the effectiveness of FDI methods. The first part of this research focus of this chapter is to understand the nonlinear dynamic behaviour of the Chylla-Haase polymerization reactor. In this part, the mathematical model of the proposed reactor is described. The Simulink model of the proposed reactor is set up using Simulink/MATLAB. The design of Simulink model is developed based on a set of ordinary differential equations that describe the dynamic behaviour of the proposed polymerization reactor. An independent radial basis function neural networks (RBFNN) are developed and employed here for an on-line diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation (FDI) scheme is developed for open-loop exothermic semi-batch polymerization reactor described by Chylla-Haase. The independent (RBFNN) is employed here when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBF neural networks are investigated. Firstly, an independent neural network is used to model the reactor dynamics and generate residuals. Secondly, an additional RBF neural network is developed as a classifier to isolate faults from the generated residuals. In the third part of this research, a robust fault detection and isolation (FDI) scheme is developed to monitor the Chylla-Haase polymerization reactor, when it is under the cascade PI control. This part is really challenging task as the controller output cannot be designed when the reactor is under closed-loop control, and the control action will correct small changes of the states caused by faults. The proposed FDI strategy employed a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. In this research, an independent MLP neural network is implemented here to generate residuals for detection task. And another RBF is applied for isolation task performing as a classifier. The fault diagnosis scheme is developed for a Chylla-Haase reactor under open-loop and closed-loop control system. The comparison between these two neural network architectures (MPL and RBF) are shown that RBF configuration trained by (RLS) algorithm have several advantages. The first one is greater efficiency in finding optimal weights for field strength prediction in complex dynamic systems. The RBF configuration is less complex network that results in faster convergence. The training algorithms (RLs and ROLS) that used for training RBFNN in chapter (4) and (5) have proven to be efficient, which results in significant faster computer time in comparison to back-propagation one. Another fault diagnosis (FD) scheme is developed in this research for an exothermic semi-batch polymerization reactor. The scheme includes two parts: the first part is to generate residual using an extended Kalman filter (EKF), and the second part is the decision making to report fault using a standardized hypothesis of statistical tests. The FD simulation results are presented to demonstrate the effectiveness of the proposed method. In the lase section of this research, a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear dynamic system. A general framework is developed for model-based fault detection and diagnosis using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. The changes in the system dynamics due to fault are modelled as nonlinear functions of the state, while the time profile of the fault is assumed to be exponentially developing. The changes in the system dynamics are monitored by an on-line approximation model, which is used for detecting the failures. A systematic procedure for constructing nonlinear estimation algorithm is developed, and a stable learning scheme is derived using Lyapunov theory. Simulation studies are used to illustrate the results and to show the effectiveness of the fault diagnosis methodology. Finally, the success of the proposed fault diagnosis methods illustrates the potential of the application of an independent RBFNN, an independent MLP, an Extended kalman filter and an adaptive nonlinear observer based FD, to chemical reactors

Observer based active fault tolerant control of descriptor systems

The active fault tolerant control (AFTC) uses the information provided by fault detection and fault diagnosis (FDD) or fault estimation (FE) systems offering an opportunity to improve the safety, reliability and survivability for complex modern systems. However, in the majority of the literature the roles of FDD/FE and reconfigurable control are described as separate design issues often using a standard state space (i.e. non-descriptor) system model approach. These separate FDD/FE and reconfigurable control designs may not achieve desired stability and robustness performance when combined within a closed-loop system.This work describes a new approach to the integration of FE and fault compensation as a form of AFTC within the context of a descriptor system rather than standard state space system. The proposed descriptor system approach has an integrated controller and observer design strategy offering better design flexibility compared with the equivalent approach using a standard state space system. An extended state observer (ESO) is developed to achieve state and fault estimation based on a joint linear matrix inequality (LMI) approach to pole-placement and H∞ optimization to minimize the effects of bounded exogenous disturbance and modelling uncertainty. A novel proportional derivative (PD)-ESO is introduced to achieve enhanced estimation performance, making use of the additional derivative gain. The proposed approaches are evaluated using a common numerical example adapted from the recent literature and the simulation results demonstrate clearly the feasibility and power of the integrated estimation and control AFTC strategy. The proposed AFTC design strategy is extended to an LPV descriptor system framework as a way of dealing with the robustness and stability of the system with bounded parameter variations arising from the non-linear system, where a numerical example demonstrates the feasibility of the use of the PD-ESO for FE and compensation integrated within the AFTC system.A non-linear offshore wind turbine benchmark system is studied as an application of the proposed design strategy. The proposed AFTC scheme uses the existing industry standard wind turbine generator angular speed reference control system as a “baseline” control within the AFTC scheme. The simulation results demonstrate the added value of the new AFTC system in terms of good fault tolerance properties, compared with the existing baseline system

Early warning generation for process with unknown disturbance

Process safety has paramount importance in a chemical process. A well designed control system is the first layer in a process system. The warning system works as the upper protection layer above the control system. It alerts the operators when the control system fails to prevent an undesired situation. A typical warning system issues warnings when a monitored variable exceeds the threshold. Often these do not allow operators sufficient lead-time to take corrective actions. With the motivation of improving the operator’s working environment by providing lead-time, the current research develops a predictive warning scheme using a moving horizon technique. The main hypothesis proposed in this thesis is given the current state of process system, the future states of the system can be predicted using a suitable model of process system. If an external input disturbs the system state, the controller will try to bring the system within the desired control/safety limits of the system. A warning is issued if it is determined that the control system will not be able to keep the system withing the safety limits. Based on the hypothesis, warning systems were developed for both linear and nonlinear systems. For linear systems, using the gain of the models, a linear constrained optimization problem was formulated. Linear programming (LP) was used to determine if the system will remain within the safety limits or not. In case the LP determines that there is no feasible solution within the constrained limits, warnings are issued. The predictive warning scheme was also extended for nonlinear systems. A non-linear receding horizon predictor was used to predict the future states of the nonlinear system. However, for nonlinear system formulation leads to nonlinear constrained optimization problem, where the constraints are the safety limits. Controller’s ability to keep the predicted states inside the safety limit was checked using a feasibility test algorithm. The algorithm uses a constraint separation method with weighting functions to determine the existence of a feasible solution. The algorithm calculates the global minimum of the objective function. If the global minimum of the objective function is positive, it signifies no feasible solution within the input and output constraints of the system and a warning is issued. Prediction of the effect of the disturbances requires the knowledge of the disturbances. In process industries, disturbances are often unmeasured. This thesis also investigates the estimation of unknown disturbances. An iterative Expectation Minimization (EM) algorithm was proposed for the estimation of the unknown states and disturbances of nonlinear systems. Efficacy of the proposed methods was shown through a number of case studies. The warning system for the linear system was simulated on a virtual plant of a continuous stirred tank heater (CSTH). The nonlinear warning system was implemented on a continuous stirred tank reactor (CSTR). Both case studies showed that, the proposed method was capable of providing a warning earlier than the traditional methods that issues warning based on the measured signals