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Author : Xiangyu Kong
Publisher : Springer Nature
Page : 324 pages
File Size : 51,5 Mb
Release : 2024-05-21
Category : Electronic
ISBN : 9789819987757
Author : Uwe Kruger,Lei Xie
Publisher : John Wiley & Sons
Page : 1 pages
File Size : 48,6 Mb
Release : 2012-08-22
Category : Mathematics
ISBN : 9780470517246
The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications. Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry. This book: Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement. Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. Features a supplementary website including Matlab algorithms and data sets. This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.
Author : Zhiqiang Ge,Zhihuan Song
Publisher : Springer Science & Business Media
Page : 204 pages
File Size : 47,9 Mb
Release : 2012-11-28
Category : Technology & Engineering
ISBN : 9781447145134
Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Author : Uwe Kruger,Lei Xie
Publisher : John Wiley & Sons
Page : 486 pages
File Size : 48,8 Mb
Release : 2012-10-04
Category : Mathematics
ISBN : 9780470028193
The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications. Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry. This book: Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement. Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. Features a supplementary website including Matlab algorithms and data sets. This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.
Author : Jan Peter Hessling
Publisher : BoD – Books on Demand
Page : 228 pages
File Size : 51,8 Mb
Release : 2017-07-05
Category : Computers
ISBN : 9789535132790
Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.
Author : Zhiwen Chen
Publisher : Springer
Page : 112 pages
File Size : 47,5 Mb
Release : 2017-01-02
Category : Technology & Engineering
ISBN : 9783658167561
Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.
Author : Jing Wang,Jinglin Zhou,Xiaolu Chen
Publisher : Springer Nature
Page : 277 pages
File Size : 53,7 Mb
Release : 2022-01-03
Category : Technology & Engineering
ISBN : 9789811680441
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.
Author : Chris Aldrich,Lidia Auret
Publisher : Springer Science & Business Media
Page : 374 pages
File Size : 43,7 Mb
Release : 2013-06-15
Category : Computers
ISBN : 9781447151852
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.
Author : Kai Zhang
Publisher : Springer
Page : 153 pages
File Size : 49,5 Mb
Release : 2016-10-04
Category : Computers
ISBN : 9783658159719
The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.
Author : Evan L. Russell,Leo H. Chiang,Richard D. Braatz
Publisher : Springer Science & Business Media
Page : 193 pages
File Size : 50,7 Mb
Release : 2012-12-06
Category : Science
ISBN : 9781447104094
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 : L.H. Chiang,E.L. Russell,R.D. Braatz
Publisher : Springer Science & Business Media
Page : 281 pages
File Size : 52,9 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781447103479
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 : Yi Chai,Shangfu Li
Publisher : John Wiley & Sons
Page : 330 pages
File Size : 48,5 Mb
Release : 2016-01-08
Category : Science
ISBN : 9781118890004
A comprehensive exposition of the theory and techniques of fault identification and decision theory when applied to complex systems shows how modern computer analysis and diagnostic methods might be applied to launch vehicle design, checkout, and launch the space checkout system is a specialized area which is rarely explored in terms of the intelligent techniques and approaches involved an original view combining modern theory with well-established research material, inviting a contemporary approach to launch dynamics highlights the advanced research works in the field of testing, control and decision-making for space launch presented in a very well organized way and the technical level is very high
Author : Mohamed Ouhsain
Publisher : Unknown
Page : 0 pages
File Size : 51,6 Mb
Release : 2007
Category : Electronic
ISBN : OCLC:1108676603
The great challenge in quality control and process management is to devise computationally efficient algorithms to detect and diagnose faults. Currently, univariate statistical process control is an integral part of basic quality management and quality assurance practices used in the industry. Unfortunately, most data and process variables are inherently multivariate and need to be modelled accordingly. Major barriers such as higher complexity and harder interpretation have limited their application by both engineers and operators. Motivated by the lack of techniques dedicated in monitoring highly correlated data, we introduce in this thesis new multivariate statistical process control charts using robust statistics, machine learning, and pattern recognition techniques to propose our algorithms. The core idea behind our proposed techniques is to fully explore the advantages/limitations under a wide array of environments, and to also take advantage of the latter to develop a theoretically rigorous and computationally feasible methodology for multivariate statistical process control. Illustrating experimental results demonstrate a much improved performance of the proposed approaches in comparison with existing methods currently used in the analysis and monitoring of multivariate data.
Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
Publisher : Elsevier
Page : 330 pages
File Size : 44,9 Mb
Release : 2020-07-03
Category : Technology & Engineering
ISBN : 9780128193662
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods
Author : Hong-Yue Zhang
Publisher : Elsevier
Page : 1576 pages
File Size : 48,5 Mb
Release : 2007-03-01
Category : Science
ISBN : 008055539X
The safe and reliable operation of technical systems is of great significance for the protection of human life and health, the environment, and of the vested economic value. The correct functioning of those systems has a profound impact also on production cost and product quality. The early detection of faults is critical in avoiding performance degradation and damage to the machinery or human life. Accurate diagnosis then helps to make the right decisions on emergency actions and repairs. Fault detection and diagnosis (FDD) has developed into a major area of research, at the intersection of systems and control engineering, artificial intelligence, applied mathematics and statistics, and such application fields as chemical, electrical, mechanical and aerospace engineering. IFAC has recognized the significance of FDD by launching a triennial symposium series dedicated to the subject. The SAFEPROCESS Symposium is organized every three years since the first symposium held in Baden-Baden in 1991. SAFEPROCESS 2006, the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes was held in Beijing, PR China. The program included three plenary papers, two semi-plenary papers, two industrial talks by internationally recognized experts and 258 regular papers, which have been selected out of a total of 387 regular and invited papers submitted. * Discusses the developments and future challenges in all aspects of fault diagnosis and fault tolerant control * 8 invited and 36 contributed sessions included with a special session on the demonstration of process monitoring and diagnostic software tools