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. 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.

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 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&...