Data-Driven Prediction for Industrial Processes and Their Applications

Data-Driven Prediction for Industrial Processes and Their Applications
Author: Jun Zhao
Publisher: Springer
Total Pages: 453
Release: 2018-08-20
Genre: Computers
ISBN: 3319940511

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 Smart Manufacturing Technologies and Applications

Data Driven Smart Manufacturing Technologies and Applications
Author: Weidong Li
Publisher: Springer Nature
Total Pages: 218
Release: 2021-02-20
Genre: Technology & Engineering
ISBN: 3030668495

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.

Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research

Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
Author: Chao Shang
Publisher: Springer
Total Pages: 154
Release: 2018-02-22
Genre: Technology & Engineering
ISBN: 9811066779

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.

Smart Healthcare Systems

Smart Healthcare Systems
Author: Adwitiya Sinha
Publisher: CRC Press
Total Pages: 329
Release: 2019-07-24
Genre: Computers
ISBN: 0429670281

About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

Applications of Artificial Intelligence in Process Systems Engineering

Applications of Artificial Intelligence in Process Systems Engineering
Author: Jingzheng Ren
Publisher: Elsevier
Total Pages: 542
Release: 2021-06-05
Genre: Technology & Engineering
ISBN: 012821743X

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering

Filter-Based Fault Diagnosis and Remaining Useful Life Prediction

Filter-Based Fault Diagnosis and Remaining Useful Life Prediction
Author: Yong Zhang
Publisher: CRC Press
Total Pages: 290
Release: 2023-02-10
Genre: Technology & Engineering
ISBN: 1000835944

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning. Features: Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities. Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective. Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system. Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems. Includes simulation examples in each chapter to reflect the engineering practice. This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.

Data-Driven Fault Detection for Industrial Processes

Data-Driven Fault Detection for Industrial Processes
Author: Zhiwen Chen
Publisher: Springer
Total Pages: 124
Release: 2017-01-02
Genre: Technology & Engineering
ISBN: 3658167564

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.

Technological Innovation for Industry and Service Systems

Technological Innovation for Industry and Service Systems
Author: Luis M. Camarinha-Matos
Publisher: Springer
Total Pages: 424
Release: 2019-04-29
Genre: Computers
ISBN: 3030177718

This book constitutes the refereed proceedings of the 10th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2019, held in Costa de Caparica, Portugal, in May 2019. The 36 revised full papers presented were carefully reviewed and selected from 73 submissions. The papers present selected results produced in engineering doctoral programs and focus on technological innovation for industry and serivce systems. Research results and ongoing work are presented, illustrated and discussed in the following areas: collaborative systems, collaboration and resilient systems, decision and optimization systems, assistive systems, smart environments, smart manufacturing, water monitoring systems, communication systems, and energy systems.