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Data Analytics in the Era of the Industrial Internet of Things

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This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT... Weiterlesen
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Beschreibung

This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts' decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise.
   
The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. 

Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.


Dr. Aldo Dagnino is an Industrial Engineer and received his M. A. Sc. and Ph. D degrees in the Department of Systems Design Engineering at the University of Waterloo in Canada. He has  collaborated with various universities such as North Carolina State University and  the University of Calgary where he held Adjunct Faculty appointments to conduct joint research, co-supervise graduate students, and create industrial internship programs to bridge academia with industry needs. Dr. Dagnino has 30 years' experience developing advanced software solutions for industrial applications. The main focus of his work has been to bridge the technical fields of Computer Science, Software Engineering, and Industrial Systems Engineering for the development of new production systems that will enhance environmentally sustainable industrial processes and create new job opportunities. Dr. Dagnino has also utilized the fields of Artificial Intelligence, Machine Learning, Data Mining, Operations Research, Robotics, Software Engineering, Industrial Engineering, and Manufacturing Engineering in the development of new software products and services for electronics, telecommunications, electro-mechanics, oil and gas, power generation, manufacturing, and power transmission and distribution. Dr. Dagnino led the Advanced Industrial Analytics Group at ABB US Corporate Research and is currently leading the advanced analytics activities within the ABB Global Information Systems organization.


Autorentext
Dr. Aldo Dagnino is an Industrial Engineer and received his M. A. Sc. and Ph. D degrees in the Department of Systems Design Engineering at the University of Waterloo in Canada. He has  collaborated with various universities such as North Carolina State University and  the University of Calgary where he held Adjunct Faculty appointments to conduct joint research, co-supervise graduate students, and create industrial internship programs to bridge academia with industry needs. Dr. Dagnino has 30 years' experience developing advanced software solutions for industrial applications. The main focus of his work has been to bridge the technical fields of Computer Science, Software Engineering, and Industrial Systems Engineering for the development of new production systems that will enhance environmentally sustainable industrial processes and create new job opportunities. Dr. Dagnino has also utilized the fields of Artificial Intelligence, Machine Learning, Data Mining, Operations Research, Robotics, Software Engineering, Industrial Engineering, and Manufacturing Engineering in the development of new software products and services for electronics, telecommunications, electro-mechanics, oil and gas, power generation, manufacturing, and power transmission and distribution. Dr. Dagnino led the Advanced Industrial Analytics Group at ABB US Corporate Research and is currently leading the advanced analytics activities within the ABB Global Information Systems organization.


Inhalt
Chapter 1: Industrial Internet of Things Framework
Layered View of IIoT systems
Analytics Capabilities in IIoT Systems Can Increase Job Satisfaction 
Examples of IIoT Business Models 
Power Distribution Systems in the IIoT
IIoT 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 
References
Chapter 2: Industrial Analytics
Machine Learning 
Supervised Machine Learning 
Decision Trees for Classification and Regression
Random Forest Classification and Regression 
Neural Networks for Classification and Regression 
Sentiment Analysis and Machine Learning 
Support Vector Machines 
Unsupervised Machine Learning 
Association Rule Mining
K-Means Clustering 
Anomaly Detection Machine Learning
Analytic 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 Models
Substation Prediction Models
Infrastructure Prediction Models
Feeder Prediction Models
Proactive Fault Analytics Helps Improving the Business Model and Employee Satisfaction
References
Chapter 4: Analyzing Events and Alarms in Control Systems 
Problem Statement
Background
Anatomy of Alarms in IIoT Distributed Control Systems
Alarm Data 
Alarm Management Analytics Models 
Sequence Pattern Mining and Association Rule Mining
Alarm Baskets 
Alarm De-chattering Analysis
Alarm Sequence Analysis
Measures of Significance or Metrics for Sequence Analysis
Enhancing Expert Knowledge of Plant Operations Through Advanced Analytics Alarm Management
References
Chapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants 
Problem Statement 
Background
Turbine Telemetry Data
Analytics for Anomaly Detection of Rotating Machines
Statistical Analysis of Turbine Data
Clustering Analysis of Turbine Data
Anomaly Detection Using Connectivity-based Outlier Factor
Enhancing Domain Knowledge of Power Engineers Through Anomaly Detection System
References
Chapter 6: Machine Learning Recommender for New Products and Services 
Problem Statement
Background
Historical Data
Product and Services Recommender Analytics
Customer Classification Analytics
Market Basket Analysis
Sentiment Analysis 
Enhancing Domain Knowledge of Service Engineer Salespeople Through the Product and Services Recommender System 
References
Chapter 7: Managing Analytic Projects in the IIoT Enterprise 
Definition Phases of an Analytics Project in the IIoT Enterprise 
Delivery Framework for IIoT Advanced Analytics Projects
Sustaining Phase 
Requirements Engineering 

Produktinformationen

Titel: Data Analytics in the Era of the Industrial Internet of Things
Autor:
EAN: 9783030631390
Digitaler Kopierschutz: Adobe-DRM
Format: E-Book (pdf)
Hersteller: Springer-Verlag
Genre: Datenkommunikation, Netzwerke
Anzahl Seiten: 133
Veröffentlichung: 05.02.2021
Dateigrösse: 4.0 MB