Data Analytics in the Era of the Industrial Internet of Things

ISBN-13: 9783030631413 / Angielski / Miękka / 2022 / 152 str.

Aldo Dagnino

Opis

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.

Spis treści

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 

Project Management Process 

Data Preparation Phase 

Analytics and Implementation Phase 

Technical Solution Process 

Verification and Validation Processes 

Agile Kanban Development Lifecycle 

Barriers for the Implementation of Analytic Projects in the IIoT

Lack of clear business value 

Absence of Large User Base

Takes Too Long to Develop the Solution 

Organization Focused on Short-term Gains

High-level Complexity 

Organizational Readiness for Change 

References 

Conclusions 

List of Abbreviations 

O autorze

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.

Blurb

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.