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Author: Mahmoudreza Sharifi Publisher: ISBN: Category : Languages : en Pages :
Book Description
The purpose of this research is to address the problem of fault diagnosis of sensors which measure a set of direct redundant variables. This study proposes: 1. A method for linear senor fault diagnosis 2. An analysis of isolability and detectability of sensor faults 3. A stochastic method for the decision process 4. A nonlinear approach to sensor fault diagnosis. In this study, first a geometrical approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output model in model based methods or from a Principal Component Analysis (PCA) based model in data driven methods. Using this residual generator and the assumption of white Gaussian noise, the effect of noise on the isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Next, for the decision process a probabilistic approach to sensor fault diagnosis is presented. Unlike most existing probabilistic approaches to fault diagnosis, which are based on Bayesian Belief Networks, in this approach the probabilistic model is directly extracted from a parity equation. The relevant parity equation can be found using a model of the system or through PCA analysis of data measured from the system. In addition, a sensor detectability index is introduced that specifies the level of detectability of sensor faults in a set of redundant sensors. This index depends only on the internal relationships of the variables of the system and noise level. Finally, the proposed linear sensor fault diagnosis approach has been extended to nonlinear method by separating the space of measurements into several local linear regions. This classification has been performed by application of Mixture of Probabilistic PCA (MPPCA). The proposed linear and nonlinear methods are tested on three different systems. The linear method is applied to sensor fault diagnosis in a smart structure and to the Tennessee Eastman process model, and the nonlinear method is applied to a data set collected from a fully instrumented HVAC system.
Author: Mahmoudreza Sharifi Publisher: ISBN: Category : Languages : en Pages :
Book Description
The purpose of this research is to address the problem of fault diagnosis of sensors which measure a set of direct redundant variables. This study proposes: 1. A method for linear senor fault diagnosis 2. An analysis of isolability and detectability of sensor faults 3. A stochastic method for the decision process 4. A nonlinear approach to sensor fault diagnosis. In this study, first a geometrical approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output model in model based methods or from a Principal Component Analysis (PCA) based model in data driven methods. Using this residual generator and the assumption of white Gaussian noise, the effect of noise on the isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Next, for the decision process a probabilistic approach to sensor fault diagnosis is presented. Unlike most existing probabilistic approaches to fault diagnosis, which are based on Bayesian Belief Networks, in this approach the probabilistic model is directly extracted from a parity equation. The relevant parity equation can be found using a model of the system or through PCA analysis of data measured from the system. In addition, a sensor detectability index is introduced that specifies the level of detectability of sensor faults in a set of redundant sensors. This index depends only on the internal relationships of the variables of the system and noise level. Finally, the proposed linear sensor fault diagnosis approach has been extended to nonlinear method by separating the space of measurements into several local linear regions. This classification has been performed by application of Mixture of Probabilistic PCA (MPPCA). The proposed linear and nonlinear methods are tested on three different systems. The linear method is applied to sensor fault diagnosis in a smart structure and to the Tennessee Eastman process model, and the nonlinear method is applied to a data set collected from a fully instrumented HVAC system.
Author: Wei Zhang Publisher: IntechOpen ISBN: 9789533070377 Category : Technology & Engineering Languages : en Pages : 512
Book Description
In this book, a number of innovative fault diagnosis algorithms in recently years are introduced. These methods can detect failures of various types of system effectively, and with a relatively high significance.
Author: L.H. Chiang Publisher: Springer Science & Business Media ISBN: 1447103475 Category : Technology & Engineering Languages : en Pages : 281
Book Description
Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.
Author: Rolf Isermann Publisher: Springer Science & Business Media ISBN: 3540303685 Category : Technology & Engineering Languages : en Pages : 478
Book Description
With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.
Author: Juan Carlos Tudón Martínez Publisher: ISBN: Category : Languages : en Pages :
Book Description
Quick detection and correct isolation of soft faults in a control system allow to improve the product quality, particularly in chemical processes, for example: an industrial heat exchanger. According to Venkatasubramanian, the fault methods can be classified as: model-based methods and historical data-based methods. In this thesis, two Fault Detection and Isolation (FDI) systems are designed and validated in the same process, i.e. in a shell and tube industrial heat exchanger. One of them is based on the Dynamic Principal Component Analysis (DPCA) method and the another one on a set of diagnostic observers. The former method requires historical data of the process, whereas, the diagnostic observers use quantitative models. Before testing both methods, they are trained in the same normal operating point. Four kinds of faults are introduced under the same process conditions in order to compare the performance of both diagnostic methods. All these fault cases are considered as soft faults in sensors or actuators; the faults are implemented with abrupt or gradual behavior. Similar metrics are defined in both FDI methods in order to analyze the desirable characteristics of any fault diagnostic system: robustness, quick detection, isolability capacity, explanation facility, false alarm rates and multiple faults identifiability. Experimental results show the principal advantages and disadvantages of both methods and allows to present a comparative table with the achieved performance of each method. This work allows to design and development both methods in parallel. The Recursive Least Squares (RLS) method is used to identify the process through a Random Binary Signal (RBS) test. The reliable model of each fault allows to design a set of diagnostic observers. On the other hand, a statistical analysis based on historical data is designed to know the operating status. The DPCA method projects the data into two new spaces in order to detect any abnormal event using a smaller number of process variables. In this manner, two methods, based on different approaches, are tested under the same experimental data. This work shows that a set of diagnostic observers can detect a soft fault in a sensor or actuator at shorter time than the DPCA method. The diagnostic observers present a lower false alarm rate than the DPCA method, when soft faults in actuators are implemented. Furthermore, diagnostic observers can identify multiple faults, whereas the DPCA method can not associate correctly the errors to the occurred faults. However, the training and testing stages of the diagnostic observers require greater computational resources than the stages of the DPCA method.
Author: Evan L. Russell Publisher: Springer Science & Business Media ISBN: 1447104099 Category : Science Languages : en Pages : 193
Book Description
Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.
Author: Sayyed Hamidreza Mousavi Publisher: LAP Lambert Academic Publishing ISBN: 9783659323843 Category : Languages : en Pages : 88
Book Description
Process history based approaches for fault diagnosis has been widely used recently. Principal Component Analysis (PCA) is one of these approaches, which is a linear approach; however most of the processes are nonlinear. Hence nonlinear extensions of the PCA have been developed. Nonlinear Principal Component Analysis (NLPCA) based on the neural networks is a common method which is used for process monitoring and fault diagnosis. NLPCA based neural networks are implemented using different methods, in this book we apply Auto-Associative Neural Networks (AANN) for implementing NLPCA. This work is aimed towards the development of an algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. Also an algorithm is developed for locating the source of the process fault.
Author: Mahmoudreza Sharifi
Author: Mahmoudreza Sharifi
Author: Wei Zhang
Author: Yvon Tharrault
Author: L.H. Chiang
Author: Rolf Isermann
Author: Hiranmayee Vedam
Author: Juan Carlos Tudón Martínez
Author: Evan L. Russell
Author: Sayyed Hamidreza Mousavi
Author: Edward Y. Bai