Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things, Hill Richard, Berry Stuart
Автор: Sabina Jeschke; Christian Brecher; Houbing Song; D Название: Industrial Internet of Things ISBN: 3319826085 ISBN-13(EAN): 9783319826080 Издательство: Springer Рейтинг: Цена: 39130.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Industrial Internet of Things and Cyber Manufacturing Systems.- An Application Map for Cyber-Physical Systems.- Cyber-Physical Electronics Production.- Cyber-physical Systems Engineering for Manufacturing.- Model-Based Engineering of Supervisory Controllers for Cyber- Physical Systems.- Formal Verification of SystemC-based Cyber Components.- Evaluation Model for Assessment of Cyber-Physical Production Systems.- CPS-based Manufacturing with Semantic Object Memories and Service Orchestration for Industrie 4.0 Applications.- Integration of a knowledge database and machine vision within a robot-based CPS.
Описание: Chapter 1: Industrial Internet of Things FrameworkLayered View of IIoT systemsAnalytics Capabilities in IIoT Systems Can Increase Job Satisfaction Examples of IIoT Business Models Power Distribution Systems in the IIoTIIoT in Process Control Alarm Management Power Generation Turbines Anomaly Detection Increase Share of Wallet of Industrial Services and Products Power Transformers and Utility Equipment Analysis Demand Forecast of Products and Spare Parts ReferencesChapter 2: Industrial AnalyticsMachine Learning Supervised Machine Learning Decision Trees for Classification and RegressionRandom Forest Classification and Regression Neural Networks for Classification and Regression Sentiment Analysis and Machine Learning Support Vector Machines Unsupervised Machine Learning Association Rule MiningK-Means Clustering Anomaly Detection Machine LearningAnalytic Conduits References Chapter 3: Machine Learning to Predict Fault Events in Power Distribution Systems Problem Statement Background Data for Forecasting Fault Events in Power Distribution Grids Forecasting Fault Events Creation of Machine Learning Models Zone Prediction ModelsSubstation Prediction ModelsInfrastructure Prediction ModelsFeeder Prediction ModelsProactive Fault Analytics Helps Improving the Business Model and Employee SatisfactionReferencesChapter 4: Analyzing Events and Alarms in Control Systems Problem StatementBackgroundAnatomy of Alarms in IIoT Distributed Control SystemsAlarm Data Alarm Management Analytics Models Sequence Pattern Mining and Association Rule MiningAlarm Baskets Alarm De-chattering AnalysisAlarm Sequence AnalysisMeasures of Significance or Metrics for Sequence AnalysisEnhancing Expert Knowledge of Plant Operations Through Advanced Analytics Alarm ManagementReferencesChapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants Problem Statement BackgroundTurbine Telemetry DataAnalytics for Anomaly Detection of Rotating MachinesStatistical Analysis of Turbine DataClustering Analysis of Turbine DataAnomaly Detection Using Connectivity-based Outlier FactorEnhancing Domain Knowledge of Power Engineers Through Anomaly Detection SystemReferencesChapter 6: Machine Learning Recommender for New Products and Services Problem StatementBackgroundHistorical DataProduct and Services Recommender AnalyticsCustomer Classification AnalyticsMarket Basket AnalysisSentiment Analysis Enhancing Domain Knowledge of Service Engineer Salespeople Through the Product and Services Recommender System ReferencesChapter 7: Managing Analytic Projects in the IIoT Enterprise Definition Phases of an Analytics Project in the IIoT Enterprise Delivery Framework for IIoT Advanced Analytics ProjectsSustaining Phase Requirements Engineering Project Management Process Data Preparation Phase Analytics and Implementation Phase Technical Solution Process Verification and Validation Processes Agile Kanban Development Lifecycle Barrie
Описание: Chapter 1: Industrial Internet of Things FrameworkLayered View of IIoT systemsAnalytics Capabilities in IIoT Systems Can Increase Job Satisfaction Examples of IIoT Business Models Power Distribution Systems in the IIoTIIoT in Process Control Alarm Management Power Generation Turbines Anomaly Detection Increase Share of Wallet of Industrial Services and Products Power Transformers and Utility Equipment Analysis Demand Forecast of Products and Spare Parts ReferencesChapter 2: Industrial AnalyticsMachine Learning Supervised Machine Learning Decision Trees for Classification and RegressionRandom Forest Classification and Regression Neural Networks for Classification and Regression Sentiment Analysis and Machine Learning Support Vector Machines Unsupervised Machine Learning Association Rule MiningK-Means Clustering Anomaly Detection Machine LearningAnalytic Conduits References Chapter 3: Machine Learning to Predict Fault Events in Power Distribution Systems Problem Statement Background Data for Forecasting Fault Events in Power Distribution Grids Forecasting Fault Events Creation of Machine Learning Models Zone Prediction ModelsSubstation Prediction ModelsInfrastructure Prediction ModelsFeeder Prediction ModelsProactive Fault Analytics Helps Improving the Business Model and Employee SatisfactionReferencesChapter 4: Analyzing Events and Alarms in Control Systems Problem StatementBackgroundAnatomy of Alarms in IIoT Distributed Control SystemsAlarm Data Alarm Management Analytics Models Sequence Pattern Mining and Association Rule MiningAlarm Baskets Alarm De-chattering AnalysisAlarm Sequence AnalysisMeasures of Significance or Metrics for Sequence AnalysisEnhancing Expert Knowledge of Plant Operations Through Advanced Analytics Alarm ManagementReferencesChapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants Problem Statement BackgroundTurbine Telemetry DataAnalytics for Anomaly Detection of Rotating MachinesStatistical Analysis of Turbine DataClustering Analysis of Turbine DataAnomaly Detection Using Connectivity-based Outlier FactorEnhancing Domain Knowledge of Power Engineers Through Anomaly Detection SystemReferencesChapter 6: Machine Learning Recommender for New Products and Services Problem StatementBackgroundHistorical DataProduct and Services Recommender AnalyticsCustomer Classification AnalyticsMarket Basket AnalysisSentiment Analysis Enhancing Domain Knowledge of Service Engineer Salespeople Through the Product and Services Recommender System ReferencesChapter 7: Managing Analytic Projects in the IIoT Enterprise Definition Phases of an Analytics Project in the IIoT Enterprise Delivery Framework for IIoT Advanced Analytics ProjectsSustaining Phase Requirements Engineering Project Management Process Data Preparation Phase Analytics and Implementation Phase Technical Solution Process Verification and Validation Processes Agile Kanban Development Lifecycle Barrie
Описание: Provides everything readers need to know for applying the power of informatics to materials science There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials. Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others. -Bridges the gap between materials science and informatics -Covers all the known methodologies and applications of materials informatics -Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials -Examines the state-of-the-art software and tools being used today Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.
Автор: Samanta Название: Lean Problem Solving and QC Tools for Industrial Engineers ISBN: 1138338494 ISBN-13(EAN): 9781138338494 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book enables the reader with the knowledge of QC tools and techniques and their systematic usage in practical problem situations. The learning of 7 QC tools and A3 strategy in this book will help thought process in a structured manner to make the best of time and available resources for an industrial engineer.
Описание: Predictive analytics is an evolving field and has applications across all domains and sectors. This book will introduce to the reader the concept of predictive analytics and cover in detail the predictive analytic models, tools and techniques involved. The book will also cover the applications of predictive analytics in various domains including health care, banking, agriculture, retailing, sports and industries using smart grid and industrial drivers with real world scenarios. This book covers performance improvement and enhancement techniques with the aid of intelligent predictive analytical algorithms to predict future patterns. This would be a handy guide covering all steps from identification of the problem, preparing the data, model building and recommending solutions. Hence, the readers can experience the various types of performance improvement techniques and implement them in their specific domain.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Автор: Pawan Negi, Mangey Ram, Om Prakash Yadav Название: Basics of CNC Programming ISBN: 8770220433 ISBN-13(EAN): 9788770220439 Издательство: Taylor&Francis Рейтинг: Цена: 14086.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Before the introduction of automatic machines and automation, industrial manufacturing of machines and their parts for the key industries were made though manually operated machines. Due to this, manufacturers could not make complex profiles or shapes with high accuracy. As a result, the production rate tended to be slow, production costs were very high, rejection rates were high and manufacturers often could not complete tasks on time. Industry was boosted by the introduction of the semi-automatic manufacturing machine, known as the NC machine, which was introduced in the 1950’s at the Massachusetts Institute of Technology in the USA. After these NC machine started to be used, typical profiles and complex shapes could get produced more readily, which in turn lead to an improved production rate with higher accuracy. Thereafter, in the 1970’s, an even larger revolutionary change was introduced to manufacturing, namely the use of the CNC machine (Computer Numerical Control). Since then, CNC has become the dominant production method in most manufacturing industries, including automotive, aviation, defence, oil and gas, medical, electronics industry, and the optical industry. Basics of CNC Programming describes how to design CNC programs, and what cutting parameters are required to make a good manufacturing program. The authors explain about cutting parameters in CNC machines, such as cutting feed, depth of cut, rpm, cutting speed etc., and they also explain the G codes and M codes which are common to CNC. The skill-set of CNC program writing is covered, as well as how to cut material during different operations like straight turning, step turning, taper turning, drilling, chamfering, radius profile, profile turning etc. In so doing, the authors cover the level of CNC programming from basic to industrial format. Drawings and CNC programs to practice on are also included for the reader.
Автор: Seeram Ramakrishna, Sunpreet Singh, Chander Prakas Название: Additive Manufacturing: Foundation Knowledge For The Beginners ISBN: 9811224811 ISBN-13(EAN): 9789811224812 Издательство: World Scientific Publishing Рейтинг: Цена: 12672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: "This book provides the key fundamental principles, classifications, recent developments, as well as different applications of additive manufacturing technologies"--
Автор: Chishti Mohammad Ahsan, Saleem Tausifa Jan Название: Big Data Analytics for Internet of Things ISBN: 1119740754 ISBN-13(EAN): 9781119740759 Издательство: Wiley Рейтинг: Цена: 17258.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: BIG DATA ANALYTICS FOR INTERNET OF THINGS
Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field
Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security.
The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems.
With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers:
A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications
An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc.
A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics
A treatment of machine learning techniques for IoT data analytics
Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.
Автор: P. Karthikeyan, Polinpapilinho F Katina, S.P. Anandaraj Название: New Approaches to Data Analytics and Internet of Things Through Digital Twin ISBN: 1668457237 ISBN-13(EAN): 9781668457238 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. The book covers key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery.
Автор: P. Karthikeyan, Polinpapilinho F Katina, S.P. Anandaraj Название: New Approaches to Data Analytics and Internet of Things Through Digital Twin ISBN: 1668457229 ISBN-13(EAN): 9781668457221 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 42134.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. The book covers key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery.
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