Data Driven Prediction For Industrial Processes And Their Applications
DOWNLOAD
Download Data Driven Prediction For Industrial Processes And Their Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Driven Prediction For Industrial Processes And Their Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Data Driven Prediction For Industrial Processes And Their Applications
DOWNLOAD
Author : Jun Zhao
language : en
Publisher: Springer
Release Date : 2018-08-20
Data Driven Prediction For Industrial Processes And Their Applications written by Jun Zhao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-20 with Computers categories.
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
Data Driven Fault Detection For Industrial Processes
DOWNLOAD
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.
Dynamic Modeling Of Complex Industrial Processes Data Driven Methods And Application Research
DOWNLOAD
Author : Chao Shang
language : en
Publisher: Springer
Release Date : 2018-02-22
Dynamic Modeling Of Complex Industrial Processes Data Driven Methods And Application Research written by Chao Shang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-22 with Technology & Engineering categories.
This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.
Data Driven Smart Manufacturing Technologies And Applications
DOWNLOAD
Author : Weidong Li
language : en
Publisher: Springer Nature
Release Date : 2021-02-20
Data Driven Smart Manufacturing Technologies And Applications written by Weidong Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-20 with Technology & Engineering categories.
This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.
Data Driven Fault Detection And Reasoning For Industrial Monitoring
DOWNLOAD
Author : Jing Wang
language : en
Publisher: Springer Nature
Release Date : 2022-01-03
Data Driven Fault Detection And Reasoning For Industrial Monitoring written by Jing Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-03 with Technology & Engineering categories.
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.
Data Driven Technologies And Artificial Intelligence In Supply Chain
DOWNLOAD
Author : Mahesh Chand
language : en
Publisher: CRC Press
Release Date : 2023-11-23
Data Driven Technologies And Artificial Intelligence In Supply Chain written by Mahesh Chand and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-23 with Computers categories.
This book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing. "Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies" Emphasizes the impact of a data-driven supply chain on quality management "Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing" Highlights the barriers to implementing artificial intelligence in small and medium enterprises Presents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risks The book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.
Data Driven Optimization Of Manufacturing Processes
DOWNLOAD
Author : Kanak Kalita
language : en
Publisher:
Release Date : 2020
Data Driven Optimization Of Manufacturing Processes written by Kanak Kalita and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic books categories.
"This book is a compilation of chapters on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization, offering both soft computing approaches and machining processes"--
Big Data Analytics In Smart Manufacturing
DOWNLOAD
Author : P Suresh
language : en
Publisher: CRC Press
Release Date : 2022-12-14
Big Data Analytics In Smart Manufacturing written by P Suresh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-14 with Computers categories.
The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience. Features The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit. The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems. The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way. Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing. Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners.
Data Driven Methods For Fault Detection And Diagnosis In Chemical Processes
DOWNLOAD
Author : Evan L. Russell
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Data Driven Methods For Fault Detection And Diagnosis In Chemical Processes written by Evan L. Russell 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 Science categories.
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.
Advances In Knowledge Discovery And Data Mining
DOWNLOAD
Author : De-Nian Yang
language : en
Publisher: Springer Nature
Release Date :
Advances In Knowledge Discovery And Data Mining written by De-Nian Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.