Principal Component Analysis Based Fault Detection and Isolation PDF Download

Author: Jin Cao
Publisher:
ISBN:
Category :
Languages : en
Pages : 350

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Author: Yvon Tharrault
Publisher:
ISBN:
Category : Technology
Languages : en
Pages :

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Sensor Fault Detection and Isolation by Robust Principal Component Analysis.

Author: Wei Zhang
Publisher: IntechOpen
ISBN: 9789533070377
Category : Technology & Engineering
Languages : en
Pages : 512

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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: Juan Carlos Tudón Martínez
Publisher:
ISBN:
Category :
Languages : en
Pages :

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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: L.H. Chiang
Publisher: Springer Science & Business Media
ISBN: 1447103475
Category : Technology & Engineering
Languages : en
Pages : 281

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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: Sayyed Hamidreza Mousavi
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659323843
Category :
Languages : en
Pages : 88

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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: X.Z. Gao
Publisher: Springer
ISBN: 9783642112812
Category : Computers
Languages : en
Pages : 300

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Book Description
The 14th onlineWorld Conference on Soft Computing in Industrial Applications provides a unique opportunity for soft computing researchers and practitioners to publish high quality papers and discuss research issues in detail without incurring a huge cost. The conference has established itself as a truly global event on the Internet. The quality of the conference has improved over the years. The WSC14 conference has covered new trends in soft computing to state of the art applications. The conference has also added new features such as community tools, syndication, and multimedia online presentations.

Author: Gary Ray Halligan
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 0

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"In this thesis, the detection and prediction of faults in rotating machinery is undertaken and presented in two papers. In the first paper, Principal Component Analysis (PCA), a well known data-driven dimension reduction technique, is applied to data for normal operation and four fault conditions from a one-half horsepower centrifugal water pump. Fault isolation in this scheme is done by observing the location of the data points in the Principal Component domain, and the time to failure (TTF) is calculated by applying statistical regression on the resulting PC scores. The application of the proposed scheme demonstrated that PCA was able to detect and isolate all four faults. Additionally, the TTF calculation for the impeller failure was found to yield satisfactory results. On the other hand, in the second paper, the fault detection and failure prediction are done by using a model based approach which utilizes a nonlinear observer consisting of an online approximator in discrete-time (OLAD) and a robust adaptive term. Once a fault has been detected, both the OLAD and the robust adaptive term are initiated and the OLAD then utilizes its update law to learn the unknown dynamics of the encountered fault. While in similar applications it is common to use neural networks to be used for the OLAD, in this paper an Artificial Immune System (AIS) is used for the OLAD. The proposed approach was verified through implementation on data from an axial piston pump. The scheme was able to satisfactorily detect and learn both an incipient piston wear fault and an abrupt sensor failure"--Abstract, leaf iv

Author: Lamiaa M. Elshenawy
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659303821
Category :
Languages : en
Pages : 368

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Book Description
Fault detection and isolation (FDI) techniques based on principal component analysis (PCA) are proposed. The main notable features of these techniques are stemmed from their implicity, low computational cost, and credibility to real industrial processes monitoring. Different applications are used to measure the validity, reliability and credibility of these techniques. The proposed techniques are fairly general and are applicable to most industrial processes

Author: Christopher S. Arnold
Publisher:
ISBN:
Category :
Languages : en
Pages : 214

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