Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Chris Aldrich,Lidia Auret
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Book PDF

Author : Chris Aldrich,Lidia Auret
Publisher : Springer Science & Business Media
Page : 374 pages
File Size : 54,9 Mb
Release : 2013-06-15
Category : Computers
ISBN : 9781447151852

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods by Chris Aldrich,Lidia Auret Pdf

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

Kai Zhang
Performance Assessment for Process Monitoring and Fault Detection Methods Book PDF

Author : Kai Zhang
Publisher : Springer
Page : 153 pages
File Size : 52,5 Mb
Release : 2016-10-04
Category : Computers
ISBN : 9783658159719

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Performance Assessment for Process Monitoring and Fault Detection Methods by Kai Zhang Pdf

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.

Artificial Intelligence in Models, Methods and Applications

Olga Dolinina,Igor Bessmertny,Alexander Brovko,Vladik Kreinovich,Vitaly Pechenkin,Alexey Lvov,Vadim Zhmud
Artificial Intelligence in Models Methods and Applications Book PDF

Author : Olga Dolinina,Igor Bessmertny,Alexander Brovko,Vladik Kreinovich,Vitaly Pechenkin,Alexey Lvov,Vadim Zhmud
Publisher : Springer Nature
Page : 694 pages
File Size : 41,6 Mb
Release : 2023-04-24
Category : Technology & Engineering
ISBN : 9783031229381

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Artificial Intelligence in Models, Methods and Applications by Olga Dolinina,Igor Bessmertny,Alexander Brovko,Vladik Kreinovich,Vitaly Pechenkin,Alexey Lvov,Vadim Zhmud Pdf

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

Chun-Kit Ngan
Time Series Analysis Book PDF

Author : Chun-Kit Ngan
Publisher : BoD – Books on Demand
Page : 131 pages
File Size : 47,8 Mb
Release : 2019-11-06
Category : Mathematics
ISBN : 9781789847789

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Time Series Analysis by Chun-Kit Ngan Pdf

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.

Proceedings of the 15th International Conference on Vibration Problems

Anonim
Proceedings of the 15th International Conference on Vibration Problems Book PDF

Author : Anonim
Publisher : Springer Nature
Page : 492 pages
File Size : 42,7 Mb
Release : 2024
Category : Vibration
ISBN : 9789819959228

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Proceedings of the 15th International Conference on Vibration Problems by Anonim Pdf

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

Frank (Xin X.) Zhu
Digitalization and Analytics for Smart Plant Performance Book PDF

Author : Frank (Xin X.) Zhu
Publisher : John Wiley & Sons
Page : 544 pages
File Size : 49,9 Mb
Release : 2021-04-06
Category : Technology & Engineering
ISBN : 9781119634119

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Digitalization and Analytics for Smart Plant Performance by Frank (Xin X.) Zhu Pdf

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."

Machine Learning and Data Science in the Oil and Gas Industry

Patrick Bangert
Machine Learning and Data Science in the Oil and Gas Industry Book PDF

Author : Patrick Bangert
Publisher : Gulf Professional Publishing
Page : 290 pages
File Size : 41,7 Mb
Release : 2021-03-04
Category : Science
ISBN : 9780128209141

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Machine Learning and Data Science in the Oil and Gas Industry by Patrick Bangert Pdf

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Data-Driven Fault Detection for Industrial Processes

Zhiwen Chen
Data Driven Fault Detection for Industrial Processes Book PDF

Author : Zhiwen Chen
Publisher : Springer
Page : 112 pages
File Size : 46,9 Mb
Release : 2017-01-02
Category : Technology & Engineering
ISBN : 9783658167561

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Data-Driven Fault Detection for Industrial Processes by Zhiwen Chen Pdf

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.

Distributed Computing and Artificial Intelligence, 13th International Conference

Sigeru Omatu,Ali Semalat,Grzegorz Bocewicz,Paweł Sitek,Izabela E. Nielsen,Julián A. García García,Javier Bajo
Distributed Computing and Artificial Intelligence 13th International Conference Book PDF

Author : Sigeru Omatu,Ali Semalat,Grzegorz Bocewicz,Paweł Sitek,Izabela E. Nielsen,Julián A. García García,Javier Bajo
Publisher : Springer
Page : 567 pages
File Size : 43,6 Mb
Release : 2016-05-31
Category : Technology & Engineering
ISBN : 9783319401621

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Distributed Computing and Artificial Intelligence, 13th International Conference by Sigeru Omatu,Ali Semalat,Grzegorz Bocewicz,Paweł Sitek,Izabela E. Nielsen,Julián A. García García,Javier Bajo Pdf

The 13th International Symposium on Distributed Computing and Artificial Intelligence 2016 (DCAI 2016) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the University of Sevilla (Spain), Osaka Institute of Technology (Japan), and the Universiti Teknologi Malaysia (Malaysia)

Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives

Elias G. Strangas,Guy Clerc,Hubert Razik,Abdenour Soualhi
Fault Diagnosis Prognosis and Reliability for Electrical Machines and Drives Book PDF

Author : Elias G. Strangas,Guy Clerc,Hubert Razik,Abdenour Soualhi
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 47,7 Mb
Release : 2021-11-19
Category : Technology & Engineering
ISBN : 9781119722786

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Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives by Elias G. Strangas,Guy Clerc,Hubert Razik,Abdenour Soualhi Pdf

Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives An insightful treatment of present and emerging technologies in fault diagnosis and failure prognosis In Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives, a team of distinguished researchers delivers a comprehensive exploration of current and emerging approaches to fault diagnosis and failure prognosis of electrical machines and drives. The authors begin with foundational background, describing the physics of failure, the motor and drive designs and components that affect failure and signals, signal processing, and analysis. The book then moves on to describe the features of these signals and the methods commonly used to extract these features to diagnose the health of a motor or drive, as well as the methods used to identify the state of health and differentiate between possible faults or their severity. Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives discusses the tools used to recognize trends towards failure and the estimation of remaining useful life. It addresses the relationships between fault diagnosis, failure prognosis, and fault mitigation. The book also provides: A thorough introduction to the modes of failure, how early failure precursors manifest themselves in signals, and how features extracted from these signals are processed A comprehensive exploration of the fault diagnosis, the results of characterization, and how they used to predict the time of failure and the confidence interval associated with it A focus on medium-sized drives, including induction, permanent magnet AC, reluctance, and new machine and drive types Perfect for researchers and students who wish to study or practice in the rea of electrical machines and drives, Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives is also an indispensable resource for researchers with a background in signal processing or statistics.

Machine Learning and Data Science in the Power Generation Industry

Patrick Bangert
Machine Learning and Data Science in the Power Generation Industry Book PDF

Author : Patrick Bangert
Publisher : Elsevier
Page : 276 pages
File Size : 48,8 Mb
Release : 2021-01-14
Category : Technology & Engineering
ISBN : 9780128226001

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Machine Learning and Data Science in the Power Generation Industry by Patrick Bangert Pdf

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems

Luis Carlos Méndez-González,Luis Alberto Rodríguez-Picón,Iván Juan Carlos Pérez Olguín
Innovation and Competitiveness in Industry 4 0 Based on Intelligent Systems Book PDF

Author : Luis Carlos Méndez-González,Luis Alberto Rodríguez-Picón,Iván Juan Carlos Pérez Olguín
Publisher : Springer Nature
Page : 338 pages
File Size : 50,9 Mb
Release : 2023-06-16
Category : Technology & Engineering
ISBN : 9783031297755

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Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems by Luis Carlos Méndez-González,Luis Alberto Rodríguez-Picón,Iván Juan Carlos Pérez Olguín Pdf

This book presents a series of applications of different techniques found in Industry 4.0 with relation to productivity, continuous improvement, quality, decision systems, software development, and automation systems. The techniques used throughout this book allow the reader to replicate the results obtained towards different types of companies that wish to undertake in the new era of the digital industrial revolution. This book can also help students from different areas of engineering to understand how the use of new technologies is applied to solve current relevant problems and how they give the possibility of constant innovation in the different industrial sectors. This is accomplished through the analysis of illustrative case studies, descriptive methodologies and structured insights that are provided through the different considered techniques.

Uncertainty Quantification and Model Calibration

Jan Peter Hessling
Uncertainty Quantification and Model Calibration Book PDF

Author : Jan Peter Hessling
Publisher : BoD – Books on Demand
Page : 228 pages
File Size : 44,9 Mb
Release : 2017-07-05
Category : Computers
ISBN : 9789535132790

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Uncertainty Quantification and Model Calibration by Jan Peter Hessling Pdf

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.

Hybrid Artificial Intelligence Systems

Marios Polycarpou,André C.P.L.F. de Carvalho,Jeng-Shyang Pan,Michał Woźniak,Héctor Quintián,Emilio Corchado
Hybrid Artificial Intelligence Systems Book PDF

Author : Marios Polycarpou,André C.P.L.F. de Carvalho,Jeng-Shyang Pan,Michał Woźniak,Héctor Quintián,Emilio Corchado
Publisher : Springer
Page : 710 pages
File Size : 51,6 Mb
Release : 2014-05-21
Category : Computers
ISBN : 9783319076171

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Hybrid Artificial Intelligence Systems by Marios Polycarpou,André C.P.L.F. de Carvalho,Jeng-Shyang Pan,Michał Woźniak,Héctor Quintián,Emilio Corchado Pdf

This volume constitutes the proceedings of the 9th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2014, held in Salamanca, Spain, in June 2014. The 61 papers published in this volume were carefully reviewed and selected from 199 submissions. They are organized in topical sessions on HAIS applications; data mining and knowledge discovery; video and image analysis; bio-inspired models and evolutionary computation; learning algorithms; hybrid intelligent systems for data mining and applications and classification and cluster analysis.

Future Access Enablers for Ubiquitous and Intelligent Infrastructures

Dragan Perakovic,Lucia Knapcikova
Future Access Enablers for Ubiquitous and Intelligent Infrastructures Book PDF

Author : Dragan Perakovic,Lucia Knapcikova
Publisher : Springer Nature
Page : 416 pages
File Size : 40,7 Mb
Release : 2021-06-19
Category : Computers
ISBN : 9783030784591

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Future Access Enablers for Ubiquitous and Intelligent Infrastructures by Dragan Perakovic,Lucia Knapcikova Pdf

This book constitutes the refereed post-conference proceedings of the 5th International Conference on Future Access Enablers for Ubiquitous and Intelligent Infrastructures, FABULOUS 2021, held in May 2021. Due to COVID-19 pandemic the conference was held virtually. This year’s conference topic covers security of innovative services and infrastructure in traffic, transport and logistic ecosystems. The 30 revised full papers were carefully reviewed and selected from 60 submissions. The papers are organized in thematic sessions on: Internet of things and smart city; smart environment applications; information and communications technology; smart health applications; sustainable communications and computing infrastructures.