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

A failure diagnosis system based on a neural network classifier for the space shuttle main engine

A conceptual design of a model based failure detection and diagnosis system is developed for the space shuttle main engine. This design relies on the accurate and reliable identification of the parameters of the highly nonlinear and very complex engine. The design approach is presented in some detail and results for a failed valve are presented. These preliminary results verify that the developed parameter identification technique together with a neural network classifier can be used for this purpose

Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

On the applicability of state-of-the-art fault diagnosis methodologies to simple and complex systems

This paper performs an analysis on the applicability of state-of-the-art fault diagnosis methodologies to both simple and complex systems. Here, a complex system represents a system whose global behavior, which emerges from the interactions between its usually large number of basic components, is difficult to accurately describe via a model. First, the basic notions used in fault detection and isolation, are introduced. Then, short reviews are given for the main quantitative methods, qualitative reasoning based methods, and soft computing approaches. The next section is dedicated to recent distributed approaches to fault diagnosis of complex systems. Finally, some conclusions are given

Fault estimation and fault-tolerant control for discrete-time dynamic systems

In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques

Model based fault diagnosis and prognosis of nonlinear systems

Rapid technological advances have led to more and more complex industrial systems with significantly higher risk of failures. Therefore, in this dissertation, a model-based fault diagnosis and prognosis framework has been developed for fast and reliable detection of faults and prediction of failures in nonlinear systems. In the first paper, a unified model-based fault diagnosis scheme capable of detecting both additive system faults and multiplicative actuator faults, as well as approximating the fault dynamics, performing fault type determination and time-to-failure determination, is designed. Stability of the observer and online approximator is guaranteed via an adaptive update law. Since outliers can degrade the performance of fault diagnostics, the second paper introduces an online neural network (NN) based outlier identification and removal scheme which is then combined with a fault detection scheme to enhance its performance. Outliers are detected based on the estimation error and a novel tuning law prevents the NN weights from being affected by outliers. In the third paper, in contrast to papers I and II, fault diagnosis of large-scale interconnected systems is investigated. A decentralized fault prognosis scheme is developed for such systems by using a network of local fault detectors (LFD) where each LFD only requires the local measurements. The online approximators in each LFD learn the unknown interconnection functions and the fault dynamics. Derivation of robust detection thresholds and detectability conditions are also included. The fourth paper extends the decentralized fault detection from paper III and develops an accommodation scheme for nonlinear continuous-time systems. By using both detection and accommodation online approximators, the control inputs are adjusted in order to minimize the fault effects. Finally in the fifth paper, the model-based fault diagnosis of distributed parameter systems (DPS) with parabolic PDE representation in continuous-time is discussed where a PDE-based observer is designed to perform fault detection as well as estimating the unavailable system states. An adaptive online approximator is incorporated in the observer to identify unknown fault parameters. Adaptive update law guarantees the convergence of estimations and allows determination of remaining useful life --Abstract, page iv

AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described

Online fault detection and isolation of nonlinear systems

This paper describes an online fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output and the input of the system. A nonlinear online approximator using dynamic recurrent neural network is utilised to monitor the faults in the system. The construction and the learning algorithm of the online approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.published_or_final_versio