Author: James Arthur LeonardPublisher:
ISBN:
Category :
Languages : en
Pages : 261
Book Description
A Reliable Neural Network Architecture for Fault Diagnosis and Modelling of Chemical Processes
Author: James Arthur LeonardPublisher:
ISBN:
Category :
Languages : en
Pages : 261
Book Description
Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
Author: Krzysztof PatanPublisher: Springer Science & Business Media
ISBN: 3540798714
Category : Technology & Engineering
Languages : en
Pages : 223
Book Description
An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.
Fault Diagnosis in Chemical Processes Using Neural Network Models [microform]
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Author: Shi Jin LouPublisher: National Library of Canada = Bibliothèque nationale du Canada
ISBN: 9780612830066
Category :
Languages : en
Pages : 442
Book Description
Neural Networks in Bioprocessing and Chemical Engineering
Author: D. R. BaughmanPublisher: Academic Press
ISBN: 1483295656
Category : Science
Languages : en
Pages : 509
Book Description
Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides the opportunity for hands-on experience. An important goal of the book is to help the student or practitioner learn and implement neural networks quickly and inexpensively using commercially available, PC-based software tools. Detailed network specifications and training procedures are included for all neural network examples discussed in the book. Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclature Includes a PC-compatible disk containing input data files for examples, case studies, and practice problems Presents 10 detailed case studies Contains an extensive glossary, explaining terminology used in neural network applications in science and engineering Provides examples, problems, and ten detailed case studies of neural computing applications, including: Process fault-diagnosis of a chemical reactor Leonard Kramer fault-classification problem Process fault-diagnosis for an unsteady-state continuous stirred-tank reactor system Classification of protein secondary-structure categories Quantitative prediction and regression analysis of complex chemical kinetics Software-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessing Quality control and optimization of an autoclave curing process for manufacturing composite materials Predictive modeling of an experimental batch fermentation process Supervisory control of the Tennessee Eastman plantwide control problem Predictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems
Model-based Distributed Fault Diagnosis for Chemical Processes
Author: Sanjeev MohindraFault Detection Using Neural Network
Author: Dinie MuhammadPublisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 92
Book Description
This thesis is about the application of Artificial Neural Network (ANN) as fault detection in the chemical process plant. At the present time, the process and development in chemical plants are getting more complex and hard to control. Therefore, the needs for a system that can help to supervise and control the process in the plant have to be accomplished in order to achieve higher performance and profitability. As the emergence of Artificial Neural Network application nowadays had help to solve problems in various fields had given a great significant effect as the system are reliable to be adapted in the chemical plant. Furthermore, this thesis will be focusing more on the application of Artificial Neural Network as fault detection scheme in term of estimator and classifier in the chemical plant. Fault detection is popular in the present time as a mechanism to detect early malfunction and abnormal process or equipment in the plant. By implementing such system, we can boost up the production and the safety level of the plant. For this thesis, the Vinyl Acetate Plant had been chosen as the case study to provide the necessary data and information to run the research. Vinyl Acetate Plant process will provides a dependable source of data and an appropriate test for alternative control and optimization strategies for continuous chemical processes. -Author.
Computer Systems that Learn
Author: Sholom M. WeissPublisher: Morgan Kaufmann Publishers
ISBN:
Category : Computers
Languages : en
Pages : 248
Book Description
This text is a practical guide to classification learning systems and their applications, which learn from sample data and make predictions for new cases. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's point of view.
Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
Author: Ruqiang YanPublisher: CRC Press
ISBN: 1040026591
Category : Computers
Languages : en
Pages : 217
Book Description
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning.
Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults
Author: Nabamita Banerjee RoyPublisher: CRC Press
ISBN: 1000414906
Category : Technology & Engineering
Languages : en
Pages : 144
Book Description
Explores methods of fault identification through programming and simulation in MATLAB Examines signal processing tools and their applications with examples Provides knowledge of artificial neural networks and their applications with illustrations Uses PNN and BPNN to identify the different types of faults and obtain their corresponding locations Discusses the programming of signal processing using Wavelet Transform and S-Transform
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Author: Chris AldrichPublisher: Springer Science & Business Media
ISBN: 1447151852
Category : Computers
Languages : en
Pages : 388
Book Description
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.