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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: 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: Nabil Derbel Publisher: Springer Nature ISBN: 9811517460 Category : Technology & Engineering Languages : en Pages : 331
Book Description
This book focuses on unhealthy cyber-physical systems. Consisting of 14 chapters, it discusses recognizing the beginning of the fault, diagnosing the appearance of the fault, and stopping the system or switching to a special control mode known as fault-tolerant control. Each chapter includes the background, motivation, quantitative development (equations), and case studies/illustration/tutorial (simulations, experiences, curves, tables, etc.). Readers can easily tailor the techniques presented to accommodate their ad hoc applications.
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: Noorlisa Harun Publisher: LAP Lambert Academic Publishing ISBN: 9783659177651 Category : Languages : en Pages : 172
Book Description
Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis (FDD). Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. Multivariate analysis technique i.e Principal Component Analysis, PCA and Partial Correlation Analysis, PCorrA are utilized to determine the correlation coefficient between quality variables and process variables. A precut multicomponent distillation column that has been installed with controllers is used as the study unit operation. Improved SPC method is implemented to detect and diagnose various kinds of faults, which occur in the process. Individual charting technique such as Shewhart, Exponential Weight Moving Average (EWMA) and Moving Average and Moving Range (MAMR) charts are used to facilitate the FDD.
Author: P.S. Dhurjati Publisher: Pergamon ISBN: Category : Science Languages : en Pages : 428
Book Description
The field of "On-Line Fault Detection and Supervision in the Chemical Process Industries" is relatively young. Major activity in this area has taken place only in the last fifteen years. The goals of the first workshop in Delaware were to discuss various methodologies necessary for solving industrial problems in fault diagnosis/supervision and to encourage interactions between academia and industry. This workshop also focused on development and evaluation of methodologies for on-line fault detection and supervision in the chemical process industries. It addressed theory, application, validation, performance and evaluation of methodologies such as parameter estimation, observers, parity equations, signal analysis methods, classification, rule-based systems with probabilistic approaches, fuzzy logic and neural networks. There are several trends that make the topic of this workshop especially relevant in today's world. The first is the tremendous advances made in automation and information technology that can potentially bring in an ever-increasing amount of information on to computer screens in the operating room of a plant. Avoiding problems of information overload and converting plant data to "on-line useful knowledge" is a key challenge. In some respects, one can draw parallels here to biological evolution where, over billions of years, human beings have evolved "mental models" to interpret the huge amount of information received through their senses. In the absence of the time advantage that evolution has had, we have to rely on methodologies such as those presented in this workshop to provide assistance to operators and engineers in interpreting plant information. A second trend that makes this field relevant in today's world is the increasing emphasis on environment and safety. Community activism and accidents such as those in Bhopal, India have caused media spotlights to be turned on the smallest of toxic releases or loss of life due to chemical accidents. The negative publicity generated by such events as well as the need to maintain the image of an environmentally conscious company make industry more sensitive to the issues of early detection of faults. The third trend that makes this field very relevant is that of the globalization of the world economy. Increasing globalization of the chemical process industry puts pressure on economic competitiveness and higher productivity. This implies reduced down-time due to faults, quick and flexible response of production to supply and demand changes, increasing reliance on automation and reduced personnel.
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: Howard Hao-Yuan Chou Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this thesis, the fault diagnosis of a heat exchanger system, consisting of two heat exchangers in series, is investigated. The system, modeled by ten first-order nonlinear differential equations, has three unknown disturbances and three output measurements. The goal is to determine the degradation levels in heat exchangers based on the sizes of the residuals generated via unknown input observers. The residuals must be robust against disturbances and sensitive to the degradation. It is proved that this is only achievable when the number of output measurements is greater than that of disturbances. Therefore, either some of the disturbances must be eliminated or more sensors must be installed. With one of the disturbances assumed constant, the degradation in the first heat exchanger can be determined accurately with reasonable precision. The addition of one more sensor results in a more precise diagnosis of the first heat exchanger. The degradation in the second heat exchanger can be determined when a second sensor is added, but the diagnosis is crude and only eighty percent accurate at best.
Author: Yimin Chen Publisher: ISBN: Category : Building Languages : en Pages : 500
Book Description
Residential and commercial buildings are responsible for more than 40% of the primary energy consumption in the United States. Energy wastes are estimated to reach 15% to 30% of total energy consumption due to malfunctioning sensors, components, and control systems, as well as degrading components in Heating, Ventilation, Air-conditioning (HVAC) systems and lighting systems in commercial buildings in the U.S. Studies have demonstrated that a large energy saving can be achieved by automated fault detection and diagnosis (AFDD) followed by corrections. Field studies have shown that, AFDD tools can help to reach energy savings by 5-30% from different systems such as HVAC systems, lighting systems, and refrigeration systems. At the same time, the deployment of AFDD tools can also significantly improve indoor air quality, reduce peak demand, and lower pollution. In buildings, many components or equipment are closely coupled in a HVAC system. Because of the coupling, a fault happening in one component might propagate and affect other components or subsystems. In this study, a whole building fault (WBF) is defined as a fault that occurs in one component or equipment but causes fault impacts (abnormalities) on other components and subsystems, or causes significant impacts on energy consumption and/or indoor air quality. Over the past decades, extensive research have been conducted on the development of component AFDD methods and tools. However, whole building AFDD methods, which can detect and diagnose a WBF, have not been well studied. Existing component level AFDD solutions often fail to detect a WBF or generate a high false alarm rate. Isolating a WBF is also very challenging by using component level AFDD solutions. Even with the extensive research, cost-effectiveness and scalability of existing AFDD methods are still not satisfactory. Therefore, the focus of this research is to develop cost-effective and scalable solutions for WBF AFDD. This research attempts to integrate data-driven methods with expert knowledge/rules to overcome the above-mentioned challenges. A suite of WBF AFDD methods have hence been developed, which include: 1) a weather and schedule based pattern matching method and feature based Principal Component Analysis (WPM-FPCA) method for whole building fault detection. The developed WPM-FPCA method successfully overcome the challenges such as the generation of accurate and dynamic baseline and data dimensionality reduction. And 2) a data-driven and expert knowledge/rule based method using both Bayesian Network (BN) and WPM for WBF diagnosis. The developed WPM-BN method includes a two-layer BN structure model and BN parameter model which are either learned from baseline data or developed from expert knowledge. Various WBFs have been artificially implemented in a real demo building. Building operation data which include baseline data, data that contain naturally-occurred faults and artificially implemented faults are collected and analyzed. Using the collected real building data, the developed methods are evaluated. The evaluation demonstrates the efficacy of the developed methods to detect and diagnose a WBF, as well as their implementation cost-effectiveness.
Author: Ziyun Wang Publisher: Springer ISBN: 9789811659614 Category : Technology & Engineering Languages : en Pages : 0
Book Description
The book provides fault detection and diagnosis approaches from the perspective of filtering analysis. In order to design fault detection filters, it uses set-membership principles to deal with the unknown but bounded noise term. Some regular geometric spaces are introduced, such as the ellipsoid, polyhedron, interval, to describe the feasible parameter sets of the given system. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. Some typical application cases are studied for fault detection and diagnosis in detail, which are power converter, permanent magnet synchronous motor, pitch system of wind turbine. Given its scope, the book offers a valuable guide for students, teachers, engineers and researchers in the field of fault detection and diagnosis.
Author: Juan Carlos Tudón Martínez
Author: Juan Carlos Tudón Martínez
Author: Nabil Derbel
Author: L.H. Chiang
Author: Noorlisa Harun
Author: P.S. Dhurjati
Author: Rolf Isermann
Author: Howard Hao-Yuan Chou
Author: Yimin Chen
Author: Qinghua Zhang
Author: Ziyun Wang