Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods
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Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods
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Author : Chris Aldrich
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-15
Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods written by Chris Aldrich and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-15 with Computers categories.
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.
Performance Assessment For Process Monitoring And Fault Detection Methods
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Author : Kai Zhang
language : en
Publisher: Springer
Release Date : 2016-10-04
Performance Assessment For Process Monitoring And Fault Detection Methods written by Kai Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-04 with Computers categories.
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.
Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2024-01-12
Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-12 with Computers categories.
This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring , and predictive maintenance solutions in process industry . The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications. Broadly, the book covers the following: Exploratory analysis of process data Best practices for process monitoring and predictive maintenance solutions Univariate monitoring via control charts and time series data mining Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.) Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes Fault detection and diagnosis of rotating machinery using vibration data Remaining useful life predictions for predictive maintenance
Data Mining And Knowledge Discovery For Process Monitoring And Control
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Author : Xue Z. Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Data Mining And Knowledge Discovery For Process Monitoring And Control written by Xue Z. Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
Artificial Intelligence In Models Methods And Applications
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Author : Olga Dolinina
language : en
Publisher: Springer Nature
Release Date : 2023-04-24
Artificial Intelligence In Models Methods And Applications written by Olga Dolinina and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-24 with Technology & Engineering categories.
This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.
Time Series Analysis
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Author : Chun-Kit Ngan
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-11-06
Time Series Analysis written by Chun-Kit Ngan and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with Mathematics categories.
This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.
Fault Detection And Diagnosis In Industrial Systems
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Author : L.H. Chiang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Fault Detection And Diagnosis In Industrial Systems written by L.H. Chiang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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.
Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches
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Author : Fouzi Harrou
language : en
Publisher: Elsevier
Release Date : 2020-07-03
Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches written by Fouzi Harrou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Technology & Engineering categories.
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
Proceedings Of The 15th International Conference On Vibration Problems
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Author :
language : en
Publisher: Springer Nature
Release Date : 2024
Proceedings Of The 15th International Conference On Vibration Problems written by and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Vibration categories.
This book presents the Proceedings of the 15th International Conference on Vibration Problems (ICoVP 2023) and covers vibration problems of engineering both in theoretical and applied fields. Various topics covered in this volume are Vibration in Oil and Gas, Structural Dynamics, Structural Health Monitoring, Rotor Dynamics, Measurement Diagnostics in Vibration, Computational methods in Vibration and Wave Mechanics, Dynamics of Coupled Systems, Dynamics of Micro and Macro Systems, Multi-body dynamics, Nonlinear dynamics Reliability of dynamic systems, Vibrations due to solid/liquid phase interaction, Vibrations of transport systems, Seismic Isolation, Soil dynamics, Geotechnical earthquake engineering Dynamics of concrete structures, Underwater shock waves (Tsunami), Vibration control, uncertainty quantification and reliability analysis of dynamic structures, Vibration problems associated with nuclear power reactors, Earthquake engineering, impact and wind loading and vibration in composite structures and fracture mechanics. This book will be useful for both professionals and researchers working on vibrations problems in multidisciplinary areas.
Digitalization And Analytics For Smart Plant Performance
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Author : Frank (Xin X.) Zhu
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-06
Digitalization And Analytics For Smart Plant Performance written by Frank (Xin X.) Zhu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-06 with Technology & Engineering categories.
This book addresses the topic of integrated digitization of plants on an objective basis and in a holistic manner by sharing data, applying analytics tools and integrating workflows via pertinent examples from industry. It begins with an evaluation of current performance management practices and an overview of the need for a "Connected Plant" via digitalization followed by sections on "Connected Assets: Improve Reliability and Utilization," "Connected Processes: Optimize Performance and Economic Margin " and "Connected People: Digitalizing the Workforce and Workflows and Developing Ownership and Digital Culture," then culminating in a final section entitled "Putting All Together Into an Intelligent Digital Twin Platform for Smart Operations and Demonstrated by Application cases."