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Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches
  • Author : Fouzi Harrou
  • Publsiher : Elsevier
  • Release : 03 July 2020
  • ISBN : 0128193662
  • Pages : 328 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKStatistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Summary:
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


Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
  • Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
  • Publisher : Elsevier
  • Release : 03 July 2020
GET THIS BOOKStatistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

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,


Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
  • Author : Chris Aldrich,Lidia Auret
  • Publisher : Springer Science & Business Media
  • Release : 15 June 2013
GET THIS BOOKUnsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

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


Multivariate Statistical Process Control

Multivariate Statistical Process Control
  • Author : Zhiqiang Ge,Zhihuan Song
  • Publisher : Springer Science & Business Media
  • Release : 28 November 2012
GET THIS BOOKMultivariate Statistical Process Control

Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been



Advances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing

Advances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing
  • Author : Ilkyeong Moon,Gyu M. Lee,Jinwoo Park,Dimitris Kiritsis,Gregor von Cieminski
  • Publisher : Springer
  • Release : 24 August 2018
GET THIS BOOKAdvances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing

The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production


Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
  • Author : L.H. Chiang,E.L. Russell,R.D. Braatz
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKFault Detection and Diagnosis in Industrial Systems

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.


Anomaly Detection Principles and Algorithms

Anomaly Detection Principles and Algorithms
  • Author : Kishan G. Mehrotra,Chilukuri K. Mohan,HuaMing Huang
  • Publisher : Springer
  • Release : 18 November 2017
GET THIS BOOKAnomaly Detection Principles and Algorithms

This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which


Beginning Anomaly Detection Using Python-Based Deep Learning

Beginning Anomaly Detection Using Python-Based Deep Learning
  • Author : Sridhar Alla,Suman Kalyan Adari
  • Publisher : Apress
  • Release : 10 October 2019
GET THIS BOOKBeginning Anomaly Detection Using Python-Based Deep Learning

Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book


Statistical Analysis of Profile Monitoring

Statistical Analysis of Profile Monitoring
  • Author : Rassoul Noorossana,Abbas Saghaei,Amirhossein Amiri
  • Publisher : John Wiley & Sons
  • Release : 09 September 2011
GET THIS BOOKStatistical Analysis of Profile Monitoring

A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods Statistical profile monitoring is an area of statistical quality control that is growing in significance for researchers and practitioners, specifically because of its range of applicability across various service and manufacturing settings. Comprised of contributions from renowned academicians and practitioners in the field, Statistical Analysis of Profile Monitoring presents the latest state-of-the-art research on the use of control charts to monitor process and product quality profiles. The book


Data-Driven Prediction for Industrial Processes and Their Applications

Data-Driven Prediction for Industrial Processes and Their Applications
  • Author : Jun Zhao,Wei Wang,Chunyang Sheng
  • Publisher : Springer
  • Release : 20 August 2018
GET THIS BOOKData-Driven Prediction for Industrial Processes and Their Applications

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


Big Data Application in Power Systems

Big Data Application in Power Systems
  • Author : Reza Arghandeh,Yuxun Zhou
  • Publisher : Elsevier
  • Release : 27 November 2017
GET THIS BOOKBig Data Application in Power Systems

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous


Financial Signal Processing and Machine Learning

Financial Signal Processing and Machine Learning
  • Author : Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov
  • Publisher : John Wiley & Sons
  • Release : 31 May 2016
GET THIS BOOKFinancial Signal Processing and Machine Learning

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions,


Modeling and Control of Batch Processes

Modeling and Control of Batch Processes
  • Author : Prashant Mhaskar,Abhinav Garg,Brandon Corbett
  • Publisher : Springer
  • Release : 28 November 2018
GET THIS BOOKModeling and Control of Batch Processes

Modeling and Control of Batch Processes presents state-of-the-art techniques ranging from mechanistic to data-driven models. These methods are specifically tailored to handle issues pertinent to batch processes, such as nonlinear dynamics and lack of online quality measurements. In particular, the book proposes: a novel batch control design with well characterized feasibility properties; a modeling approach that unites multi-model and partial least squares techniques; a generalization of the subspace identification approach for batch processes; and applications to several detailed case studies,


Hydrological Data Driven Modelling

Hydrological Data Driven Modelling
  • Author : Renji Remesan,Jimson Mathew
  • Publisher : Springer
  • Release : 03 November 2014
GET THIS BOOKHydrological Data Driven Modelling

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/