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.

Proceedings Of The 15th International Conference On Vibration Problems

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Author : Sadok Sassi
language : en
Publisher: Springer Nature
Release Date : 2024-03-19

Proceedings Of The 15th International Conference On Vibration Problems written by Sadok Sassi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-19 with Technology & Engineering 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 dynamicsReliability 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.

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.

Machine Learning And Data Science In The Oil And Gas Industry

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Author : Patrick Bangert
language : en
Publisher: Gulf Professional Publishing
Release Date : 2021-03-04

Machine Learning And Data Science In The Oil And Gas Industry written by Patrick Bangert and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-04 with Science categories.

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

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Author : Zhiwen Chen
language : en
Publisher: Springer
Release Date : 2017-01-02

Data Driven Fault Detection For Industrial Processes written by Zhiwen Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-02 with Technology & Engineering categories.

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.

Condition Monitoring With Vibration Signals

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Author : Hosameldin Ahmed
language : en
Publisher: John Wiley & Sons
Release Date : 2020-01-07

Condition Monitoring With Vibration Signals written by Hosameldin Ahmed 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 2020-01-07 with Technology & Engineering categories.

Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Machine Learning And Data Science In The Power Generation Industry

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Author : Patrick Bangert
language : en
Publisher: Elsevier
Release Date : 2021-01-14

Machine Learning And Data Science In The Power Generation Industry written by Patrick Bangert and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-14 with Technology & Engineering categories.

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

Fault Diagnosis Prognosis And Reliability For Electrical Machines And Drives

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Author : Elias G. Strangas
language : en
Publisher: John Wiley & Sons
Release Date : 2021-10-26

Fault Diagnosis Prognosis And Reliability For Electrical Machines And Drives written by Elias G. Strangas 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 2021-10-26 with Technology & Engineering categories.

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.

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.