Process Control System Fault Diagnosis

Autor: Ruben Gonzalez
Publisher: John Wiley & Sons
ISBN: 1118770617
File Size: 46,67 MB
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Process Control System Fault Diagnosis: A Bayesian Approach Ruben T. Gonzalez, University of Alberta, Canada Fei Qi, Suncor Energy Inc., Canada Biao Huang, University of Alberta, Canada Data-driven Inferential Solutions for Control System Fault Diagnosis A typical modern process system consists of hundreds or even thousands of control loops, which are overwhelming for plant personnel to monitor. The main objectives of this book are to establish a new framework for control system fault diagnosis, to synthesize observations of different monitors with a prior knowledge, and to pinpoint possible abnormal sources on the basis of Bayesian theory. Process Control System Fault Diagnosis: A Bayesian Approach consolidates results developed by the authors, along with the fundamentals, and presents them in a systematic way. The book provides a comprehensive coverage of various Bayesian methods for control system fault diagnosis, along with a detailed tutorial. The book is useful for graduate students and researchers as a monograph and as a reference for state-of-the-art techniques in control system performance monitoring and fault diagnosis. Since several self-contained practical examples are included in the book, it also provides a place for practicing engineers to look for solutions to their daily monitoring and diagnosis problems. Key features: • A comprehensive coverage of Bayesian Inference for control system fault diagnosis. • Theory and applications are self-contained. • Provides detailed algorithms and sample Matlab codes. • Theory is illustrated through benchmark simulation examples, pilot-scale experiments and industrial application. Process Control System Fault Diagnosis: A Bayesian Approach is a comprehensive guide for graduate students, practicing engineers, and researchers who are interests in applying theory to practice.

Computational Intelligence In Automotive Applications

Autor: Danil Prokhorov
Publisher: Springer
ISBN: 3540792570
File Size: 20,74 MB
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What is computational intelligence (CI)? Traditionally, CI is understood as a collection of methods from the ?elds of neural networks (NN), fuzzy logic and evolutionary computation. Various de?nitions and opinions exist, but what belongs to CI is still being debated; see, e.g., [1–3]. More recently there has been a proposal to de?ne the CI not in terms of the tools but in terms of challenging problems to be solved [4]. With this edited volume I have made an attempt to give a representative sample of contemporary CI activities in automotive applications to illustrate the state of the art. While CI researchand achievements in some specialized ?elds described (see, e.g., [5, 6]), this is the ?rst volume of its kind dedicated to automotive technology. As if re?ecting the general lack of consensus on what constitutes the ?eld of CI, this volume 1 illustrates automotive applications of not only neural and fuzzy computations which are considered to be the “standard” CI topics, but also others, such as decision trees, graphicalmodels, Support Vector Machines (SVM), multi-agent systems, etc. This book is neither an introductory text, nor a comprehensive overview of all CI research in this area. Hopefully, as a broad and representative sample of CI activities in automotive applications, it will be worth reading for both professionals and students. When the details appear insu?cient, the reader is encouraged to consult other relevant sources provided by the chapter authors.

Benefits Of Bayesian Network Models

Autor: Philippe Weber
Publisher: John Wiley & Sons
ISBN: 1119347459
File Size: 52,59 MB
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The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.

Micrornas In Malignant Tumors Of The Skin

Autor: PD Dr. Michael Sand
Publisher: Springer
ISBN: 3658127945
File Size: 40,29 MB
Format: PDF
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Michael Sand gives the reader an overview of current techniques in expression profiling of miRNAs and their maturation machinery in the skin. This book is a postdoctoral thesis on miRNAs in cutaneous malignant melanoma and non-melanoma skin cancer with a focus on the miRNA processing machinery and miRNA expression profiling. The research presented in this book was performed in the Dermatologic Surgery Section at the Department of Dermatology, Venereology and Allergology of the Ruhr-University Bochum, Germany and gives the reader an overview of current techniques in expression profiling of miRNAs and their maturation machinery in the skin.

Intelligent Fault Diagnosis And Prognosis For Engineering Systems

Autor: George Vachtsevanos
Publisher: Wiley
ISBN: 9780471729990
File Size: 25,57 MB
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Expert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis Intelligent Fault Diagnosis and Prognosis for Engineering Systems gives a complete presentation of basic essentials of fault diagnosis and failure prognosis, and takes a look at the cutting-edge discipline of intelligent fault diagnosis and failure prognosis technologies for condition-based maintenance. It thoroughly details the interdisciplinary methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment, and also presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components. Case studies are used throughout the book to illustrate enabling technologies. Intelligent Fault Diagnosis and Prognosis for Engineering Systems offers material in a holistic and integrated approach that addresses the various interdisciplinary components of the field--from electrical, mechanical, industrial, and computer engineering to business management. This invaluably helpful book: * Includes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques * Covers theory and practice in a way that is rooted in industry research and experience * Presents the only systematic, holistic approach to a strongly interdisciplinary topic

Data Driven Technology For Engineering System Health Management

Autor: Gang Niu
Publisher: Springer
ISBN: 9811020329
File Size: 18,91 MB
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This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

System Identification

Autor: Karel J. Keesman
Publisher: Springer Science & Business Media
ISBN: 9780857295224
File Size: 78,49 MB
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System Identification shows the student reader how to approach the system identification problem in a systematic fashion. The process is divided into three basic steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text. Following an introduction on system theory, particularly in relation to model representation and model properties, the book contains four parts covering: • data-based identification – non-parametric methods for use when prior system knowledge is very limited; • time-invariant identification for systems with constant parameters; • time-varying systems identification, primarily with recursive estimation techniques; and • model validation methods. A fifth part, composed of appendices, covers the various aspects of the underlying mathematics needed to begin using the text. The book uses essentially semi-physical or gray-box modeling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various control design methods with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from fill in URL here) will both help students to assimilate what they have learned and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques. Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification will help academic instructors teaching control-related to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.

Handbook Of Hybrid Systems Control

Autor: Jan Lunze
Publisher: Cambridge University Press
ISBN: 0521765056
File Size: 14,25 MB
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Setting out core theory and reviewing a range of new methods, theoretical problems and applications, this handbook shows how hybrid dynamical systems can be modelled and understood. Sixty expert authors involved in the recent research activities and industrial application studies provide practical insights on topics ranging from the theoretical investigations over computer-aided design to applications in energy management and the process industry. Structured into three parts, the book opens with a thorough introduction to hybrid systems theory, illustrating new dynamical phenomena through numerous examples. Part II then provides a survey of key tools and tool integration activities. Finally, Part III is dedicated to applications, implementation issues and system integration, considering different domains such as industrial control, automotive systems and digital networks. Three running examples are referred to throughout the book, together with numerous illustrations, helping both researchers and industry professionals to understand complex theory, recognise problems and find appropriate solutions.

Bayesian Methods For Structural Dynamics And Civil Engineering

Autor: Ka-Veng Yuen
Publisher: John Wiley & Sons
ISBN: 9780470824559
File Size: 56,66 MB
Format: PDF, ePub
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Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level – especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily pollutants generated by cars and factories within the area of concern. It is also difficult to obtain the updated air quality information of the surrounding cities. Furthermore, the meteorological conditions of the day for prediction are also uncertain. These are just some of the civil engineering examples to which Bayesian probabilistic methods are applicable. Familiarizes readers with the latest developments in the field Includes identification problems for both dynamic and static systems Addresses challenging civil engineering problems such as modal/model updating Presents methods applicable to mechanical and aerospace engineering Gives engineers and engineering students a concrete sense of implementation Covers real-world case studies in civil engineering and beyond, such as: structural health monitoring seismic attenuation finite-element model updating hydraulic jump artificial neural network for damage detection air quality prediction Includes other insightful daily-life examples Companion website with MATLAB code downloads for independent practice Written by a leading expert in the use of Bayesian methods for civil engineering problems This book is ideal for researchers and graduate students in civil and mechanical engineering or applied probability and statistics. Practicing engineers interested in the application of statistical methods to solve engineering problems will also find this to be a valuable text. MATLAB code and lecture materials for instructors available at http://www.wiley.com/go/yuen