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About this title

The Uptime® Elements Internet of Things (IoT) Knowledge Domain is a digitalization strategy framework to guide organizations in implementing IoT to achieve a business goal or aim. You can use this framework to support your solution for reliability and management of your physical and digital assets. It will help you to better develop a process and to get more value from assets by gaining greater insights from data generated from IoT devices, increasing process efficiency and reducing costs. This framework will provide a foundation and common language for IoT within your organization and across industries.

The elements within the IoT knowledge domain include:

  • SOURCE - items that generate or are sources of data;
  • CONNECT - methods of exchanging data;
  • COLLECT - preparation and storage of data;
  • ANALYZE - conversion of data into insights;
  • DO - actions taken from the insights;
  • DIGITAL TWIN - virtual representation of real-world entities and business processes;
  • TRUSTWORTHINESS - ensure security principles are applied throughout the data lifecycle;
  • DATA GOVERNANCE - oversight of organizational data.

Each of these elements is described in detail in this passport, along with common questions, concerns and issues that you should consider at every stage in implementing the IoT.

Contents

Publisher’s Note

IoT Knowledge Domain

  • Introduction
  • Background
  • Industry 4.0 and Reliability 4.0
  • Drivers for Digitalization
  • Uptime Elements and the IoT Domain
  • Digitization, Digitalization and Digital Transformation
  • Digital Line of Sight
  • Common Questions and Considerations
  • What Every Digital Reliability Leader Should Know

Source

  • Introduction
  • Key Terms and Definitions
  • What Is a Data Source?
  • What Is the Right Data Source?
  • Using Multiple Data Sources to Provide Perspective
  • The Difference Between IT and OT
  • Benefits of IT/OT Convergence
  • Security and Risks
  • What Every Digital Reliability Leader Should Know
  • Summary

Connect

  • Introduction
  • Key Terms and Definitions
  • What Is Connect?
  • Methods of Exchanging Data
  • What Every Digital Reliability Leader Should Know
  • Summary

Collect

  • Introduction
  • Key Terms and Definitions
  • What Is Collect?
  • The Importance of Context in Data Collection
  • Understanding Date Lakes and Data Warehouses
  • Times Series, Structured and Unstructured Data
  • Edge to Cloud Storage Strategies
  • The Pros and Cons of Cloud Computing
  • The Pros and Cons of Edge and Fog Computing
  • The Role and Future of the Plant Historian
  • What Every Digital Reliability Leader Should Know
  • Summary

Analyze

  • Introduction
  • Key Terms and Definitions
  • What Is Analyze>
  • Types of Industrial Analytics
  • The Use of Artificial Intelligence and Machine
  • Learning in Industrial Analytics
  • Model Development
  • Learning (Training) Methods
  • Deployment
  • What Every Digital Reliability Leader Should Know
  • Summary

Do

  • Introduction
  • Key Terms and Definitions
  • What Is Do?
  • Knowledge Management Automation
  • Workflow, Supply Chain and Scheduling Automation
  • Artificial Intelligence, Machine Learning and Deep Learning
  • Prescriptive Maintenance and Operations
  • Model Feedback
  • Advanced Control and Autonomous Operations
  • Mobility and Awareness
  • What Every Digital Reliability Leader Should Know
  • Summary

Digital Twin

  • Introduction
  • Key Terms and Definitions
  • What Is a Digital Twin?
  • Problems That Digital Twins Solve
  • The Digital Twin Is a System of Systems
  • Visualizing and Automating Reliability and Asset Management Processes
  • Digital Twins in Relation to Enterprise Information Systems and Operational Systems
  • Mapping Digital Twins to the IoT Domain Elements
  • What Every Digital Reliability Leader Should Know
  • Summary

Acknowledgment

Books