Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things, Hill Richard, Berry Stuart

Автор: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Название: Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
ISBN: 1118729277 ISBN-13(EAN): 9781118729274
Издательство: Wiley
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Цена: 118270.00 T
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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
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Цена: 93910.00 T
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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: Jeschke
Название: Industrial Internet of Things
ISBN: 3319425587 ISBN-13(EAN): 9783319425580
Издательство: Springer
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Цена: 214280.00 T
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Описание: This book develops the core system science needed to enable the development of a complex industrial internet of things/manufacturing cyber-physical systems (IIoT/M-CPS). Gathering contributions from leading experts in the field with years of experience in advancing manufacturing, it fosters a research community committed to advancing research and education in IIoT/M-CPS and to translating applicable science and technology into engineering practice.Presenting the current state of IIoT and the concept of cybermanufacturing, this book is at the nexus of research advances from the engineering and computer and information science domains. Readers will acquire the core system science needed to transform to cybermanufacturing that spans the full spectrum from ideation to physical realization.

Автор: Sabina Jeschke; Christian Brecher; Houbing Song; D
Название: Industrial Internet of Things
ISBN: 3319826085 ISBN-13(EAN): 9783319826080
Издательство: Springer
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Цена: 260870.00 T
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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.

Автор: Dagnino Aldo
Название: Data Analytics in the Era of the Industrial Internet of Things
ISBN: 3030631389 ISBN-13(EAN): 9783030631383
Издательство: Springer
Цена: 93160.00 T
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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

Автор: Dagnino Aldo
Название: Data Analytics in the Era of the Industrial Internet of Things
ISBN: 3030631419 ISBN-13(EAN): 9783030631413
Издательство: Springer
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Цена: 93160.00 T
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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

Автор: Isayev O
Название: Materials Informatics: Methods, Tools, and Applications
ISBN: 3527341218 ISBN-13(EAN): 9783527341214
Издательство: Wiley
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Цена: 102380.00 T
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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
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Цена: 91860.00 T
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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.

Автор: L. Ashok Kumar
Название: Predictive Analytics for Data Driven Decision Making: Tools and Techniques for Solving Real World Problems
ISBN: 1685076505 ISBN-13(EAN): 9781685076504
Издательство: Nova Science
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Цена: 215410.00 T
Наличие на складе: Невозможна поставка.
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.

Автор: Karen Snyder Travis
Название: Reducing Operational Costs in Composites Manufacturing
ISBN: 1138609056 ISBN-13(EAN): 9781138609051
Издательство: Taylor&Francis
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Цена: 142910.00 T
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Описание: Reducing Operational Costs in Composites Manufacturing provides organization-specific principles for managers working in the composites industry. It utilizes a "how to" format for reducing operational costs and provides examples for each principle.

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
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Цена: 84480.00 T
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Описание: "This book provides the key fundamental principles, classifications, recent developments, as well as different applications of additive manufacturing technologies"--

Автор: Parvin Hosseini Seyed Mehrshad, Azizi Aydin
Название: Big Data Approach to Firm Level Innovation in Manufacturing: Industrial Economics
ISBN: 9811562997 ISBN-13(EAN): 9789811562990
Издательство: Springer
Цена: 55890.00 T
Наличие на складе: Невозможна поставка.
Описание: This book discusses utilizing Big Data and Machine Learning approaches in investigating five aspects of firm level innovation in manufacturing;