AI Factory: Theories, Applications and Case Studies

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
About the Authors
Foreword
Preface
Acknowledgements
Prologue
Chapter 1 Introduction
1.1 AI Factory
1.1.1 Artificial Intelligence
1.1.2 Industrial Automation
1.1.3 Engineering Knowledge
1.1.4 System Understanding
1.1.5 Context Adaptation
1.1.6 Connectivity
1.1.7 Information Logistics
1.1.8 In Summary
1.2 Artificial Intelligence-Empowered Analytics
1.3 AI Revolution in Industry
1.3.1 The AI Revolution is Happening Now
1.3.1.1 Healthcare
1.3.1.2 Education
1.3.1.3 Banking, Financial Services and Insurance (BFSI)
1.3.1.4 Retail and e-Commerce
1.3.1.5 Gaming and Entertainment
1.3.2 The Road to Superintelligence
1.4 AI Winter, AI Spring
1.4.1 The First AI Spring
1.4.2 The First AI Winter
1.4.3 The Second AI Spring
1.4.4 The Second AI Winter
1.4.5 Another Spring: The DL Revolution
1.4.6 Predictions of Another AI Winter
1.4.7 Surviving the Next AI Winter: From Myths to Realities
1.5 The Value of AI
1.5.1 Challenges of AI
1.6 Power of AI vs. Human Intelligence
1.7 Technologies Powering AI: ML and DL
1.8 Perception and Cognition
References
Chapter 2 Digital Twins
2.1 Basic Concept of Digital Twin
2.2 History of DT
2.3 What is Digital Twin? Its Intrinsic Characteristics
2.3.1 Why Use Digital Twin?
2.3.2 How Does Digital Twin Work?
2.3.2.1 Digital Twin and Simulation
2.3.2.2 Digital Twin and Cyber-Physical Systems
2.4 The Evolution of Digital Twin
2.5 Data Twin and the Physical World
2.6 Data Twin and the Digital World
2.7 Useful Terms and Classifications
2.7.1 Prototypes, Instances, and Aggregates
2.7.2 Digital Twin Classes
2.7.3 Digital Twin Categories
2.8 Level of Integration
2.8.1 Digital Model
2.8.2 Digital Shadow
2.8.3 Digital Twin
2.9 Main Characteristics of Digital Twin
2.10 Modelling Digital Twins
2.10.1 Systems Modelling Language (SysML)
2.10.2 Simulation as the Basis of Digital Twin Technology
2.10.3 The Connection Between MES-Systems and Digital Twins
2.10.4 Application Tools
2.11 Smart Manufacturing: An Example of Digital Twin Development and Operation
2.11.1 Development of the Smart Factory Cell
2.11.2 Operation of the Smart Factory Cell
2.12 Some Applications of Digital Twins
2.13 Uses of Digital Twin Technology
2.13.1 Current State of the Art
2.13.1.1 Components of DT
2.13.1.2 Properties of a DT
2.13.1.3 How DT Differs From Existing Technologies
2.13.1.4 A Brief Overview of Similar Concepts that Preceded DT
2.13.1.5 Added Value of Digital Twins
2.13.2 Specific Applications of Digital Twins in Maintenance
2.14 How are Digital Twins Used in Maintenance?
2.15 Digital Twins and Predictive Maintenance
2.16 A Digital Twin Maintenance Use Case: Point Machine for Train Switches
2.17 Planning the Digital Twin
2.18 Digital Twin During Operation Phase
2.19 Hybrid Analysis and Fleet Data
2.20 Steps to Ensure Widespread Implementation of Digital Twin
2.21 Digital Twin and its Impact on Industry 4.0
References
Chapter 3 Hypes and Trends in Industry
3.1 Asset Management
3.1.1 Challenges to Asset Management
3.1.2 Intelligent Asset Management
3.1.3 Taxonomy of AAM
3.2 Tracking and Tracing in Asset Management
3.2.1 What Can be Tracked and Traced?
3.2.2 Challenges of Tracking and Tracing
3.2.3 Benefits of Tracking and Tracing
3.3 Green Industry (Sustainable)
3.3.1 Sustainability Green Industry 4.0
3.3.1.1 Sustainable Green Industry Model
3.4 Industry 4.0
3.4.1 What is Industry 4.0?
3.4.2 Talking About a Revolution: What is New in Industry 4.0?
3.4.3 On the Path to Industry 4.0: What Needs to be Done?
3.4.4 Key Paradigms of Industry 4.0
3.4.5 Four Components of Networked Production
3.4.6 Connected Technologies
3.4.7 Nine Pillars of Technological Advancement
3.4.7.1 Big Data and Analytics
3.4.7.2 Autonomous Robots
3.4.7.3 Simulation
3.4.7.4 Horizontal and Vertical System Integration
3.4.7.5 Industrial Internet of Things (IIoT)
3.4.7.6 Cybersecurity
3.4.7.7 The Cloud
3.4.7.8 Additive Manufacturing
3.4.7.9 Augmented Reality
3.4.8 Other Industry 4.0 Components
3.4.8.1 Cyber-Physical Systems (CPS)
3.4.8.2 Internet of Things (IoT)
3.4.8.3 Internet of Services
3.4.8.4 Smart Factory
3.4.9 The Impact of Industry 4.0
3.4.9.1 Quantifying the Impact
3.4.9.2 Producers: Transforming Production Processes and Systems
3.4.9.3 Manufacturing-System Suppliers: Meeting New Demands and Defining New Standards
3.4.10 How Will Industry 4.0 Impact Equipment?
3.5 Digitalisation and Digitisation
3.6 Data, Models, and Algorithm
3.7 Transformative Technologies
3.7.1 Artificial Intelligence (AI)
3.7.2 The Internet of Things (IoT)
3.7.3 Blockchain
3.7.4 Some Implications
3.8 Artificial Intelligence vs Industrial Artificial Intelligence
3.8.1 Key Elements in Industrial AI: ABCDE
3.8.2 Industrial AI Ecosystem
3.8.2.1 Data Technology
3.8.2.2 Analytics Technology
3.8.2.3 Platform Technology
3.8.2.4 Operations Technology
3.9 Autonomy and Automation
3.9.1 Autonomy and Asset Management
3.9.2 Drones and Robots
3.9.2.1 Deploying Robots
3.9.3 Strong Automation Base Layer
3.9.4 Autonomy in Industry Today
3.9.4.1 Challenges of Autonomy
3.10 Digital Transformation
3.10.1 Defining Digital Transformation
3.10.2 Digital Transformation – The Future of Predictive Maintenance
3.10.2.1 Applying Digital Transformation in Maintenance
References
Chapter 4 Data Analytics
4.1 Data-Driven and Model-Driven Approaches
4.1.1 Data Mining and Knowledge Discovery
4.2 Types of Analytics
4.2.1 Descriptive Analytics
4.2.1.1 What is Descriptive Analytics?
4.2.1.2 How Does Descriptive Analytics Work?
4.2.1.3 How is Descriptive Analytics Used?
4.2.1.4 What Can Descriptive Analytics Tell Us?
4.2.1.5 Steps in Descriptive Analytics
4.2.1.6 Benefits and Drawbacks of Descriptive Analytics
4.2.2 Diagnostic Analytics
4.2.2.1 Hypothesis Testing
4.2.2.2 Correlation vs. Causation
4.2.2.3 Diagnostic Regression Analysis
4.2.2.4 How Do You Get Started with Diagnostic Analytics?
4.2.3 Maintenance Predictive Analytics
4.2.3.1 What is Predictive Analytics?
4.2.3.2 How Does Predictive Analytics Work?
4.2.3.3 What Can Predictive Analytics Tell Us?
4.2.3.4 What Are the Advantages and Disadvantages of Predictive Analysis?
4.2.3.5 Predictive Analytics Techniques
4.2.3.6 How Can a Predictive Analytics Process Be Developed?
4.2.3.7 Predictive Maintenance Embraces Analytics
4.2.3.8 Metrics for Predictive Maintenance Analytics
4.2.3.9 Technologies Used for Predictive Maintenance Analytics
4.2.3.10 Predictive Maintenance and Data Analytics
4.2.3.11 Predictive Asset Maintenance Analytics
4.2.4 Prescriptive Analytics
4.2.4.1 What is Prescriptive Analytics?
4.2.4.2 How Does Prescriptive Analytics Work?
4.2.4.3 What Can Prescriptive Analytics Tell Us?
4.2.4.4 What Are the Advantages and Disadvantages of Prescriptive Analytics?
4.2.4.5 Getting Started in Prescriptive Analysis
4.2.4.6 Maintenance Prescriptive Analytics: A Cure for Downtime
4.2.4.7 Prescription
4.2.4.8 Scale Out
4.2.4.9 The Need For Prescriptive Analytics in Maintenance: A Case Study
4.3 Big Data Analytics Methods
4.3.1 Defining Big Data Analytics
4.3.2 Defining Big Data Via the Three Vs
4.3.2.1 Data Volume as a Defining Attribute of Big Data
4.3.2.2 Data Type Variety as a Defining Attribute of Big Data
4.3.2.3 Data Feed Velocity as a Defining Attribute of Big Data
4.3.3 Text Analytics
4.3.4 Audio Analytics
4.3.5 Video Analytics
4.3.6 Social Media Analytics
4.4 Maintenance Strategies with Big Data Analytics
4.5 Data-Driven and Model-Driven Approaches
4.5.1 Data Mining and Knowledge Discovery
4.6 Maintenance Descriptive Analytics
4.7 Maintenance Diagnostic Analytics
4.8 Maintenance Predictive Analytics
4.9 Maintenance Prescriptive Analytics
4.10 Big Data Analytics Methods
4.10.1 Text Analytics
4.10.2 Audio Analytics
4.10.3 Video Analytics
4.10.4 Social Media Analytics
4.11 Big Data Management and Governance
4.12 Big Data Access and Analysis
4.13 Big Data Visualisation
4.14 Big Data Ingestion
4.15 Big Data Cluster Management
4.16 Big Data Distributions
4.17 Data Governance
4.18 Data Access
4.19 Data Analysis
4.20 Bid Data File System
4.20.1 Quantcast File System
4.20.2 Hadoop Distributed File System
4.20.3 Cassandra File System (CFS)
4.20.4 GlusterFS
4.20.5 Lustre
4.20.6 Parallel Virtual File System
4.20.7 Orange File System (OrangeFS)
4.20.8 BeeGFS
4.20.9 MapR-FS
4.20.9.1 Kudu
References
Chapter 5 Data-Driven Decision-Making
5.1 Data for Decision-Making
5.1.1 Data-Driven Decision-Making
5.1.2 The Process of Data-Driven Decision-Making
5.1.3 The Context of Data-Driven Decision-Making
5.1.4 The Importance of Data-Driven Decision-Making
5.1.5 Common Challenges of Data-Driven Decision-Making
5.1.5.1 A Lack of Infrastructure and Tools
5.1.5.2 Poor Quality Data
5.1.5.3 Siloed Data
5.1.5.4 A Lack of Buy-In
5.1.5.5 Not Knowing How to Use Data
5.1.5.6 Being Unable to Identify Actionable Data
5.1.5.7 Too Much Focus on Data
5.1.6 Data-Driven Decision-Making for Industry 4.0 Maintenance Applications
5.1.6.1 Augmented Reality
5.1.6.2 Internet of Things
5.1.6.3 System Integration
5.1.6.4 Cloud Computing
5.1.6.5 Big Data Analytics
5.1.6.6 Cyber Security
5.1.6.7 Additive Manufacturing
5.1.6.8 Autonomous Robots
5.1.6.9 Simulation
5.1.7 Data-Driven Decision-Making Versus Intuition
5.2 Data Quality
5.2.1 eMaintenance and Data Quality
5.2.1.1 Problems in Data Quality
5.2.1.2 Data Quality in The Maintenance Phases
5.2.2 Data Quality Problems
5.3 Data Augmentation
5.3.1 Importance of Data Augmentation in Machine Learning
5.3.2 Advanced Models for Data Augmentation
5.3.3 Image Recognition and Natural Language Processing
5.3.3.1 Image Classification and Segmentation
5.3.3.2 Natural Language Processing
5.3.4 Benefits of Data Augmentation
5.3.5 Challenges of Data Augmentation
5.3.6 Data Augmentation Methods
5.3.6.1 Traditional Transformations
5.3.6.2 Generative Adversarial Networks
5.3.6.3 Texture Transfer
5.3.6.4 Convolutional Neural Networks
5.3.7 Data Augmentation for Data Management
5.3.7.1 Data Augmentation for Data Preparation
5.3.7.2 Data Augmentation for Data Integration
5.3.8 Advanced Data Augmentation
5.3.8.1 Interpolation-Based Data Augmentation
5.3.8.2 Generation-Based Data Augmentation
5.3.8.3 Learned-Data Augmentation
5.3.9 Data Augmentation with Other Learning Paradigms
5.3.9.1 Semi-Supervised and Active Learning
5.3.9.2 Weak Supervision
5.3.9.3 Pre-Training for Relational Data
5.4 Information Logistics
5.4.1 Information Logistics and eMaintenance
5.4.2 Information Logistics and Information Flow in an Era of Industry 4.0
5.4.3 Information Life Cycle
5.4.4 eMaintenance – Information Logistics for Maintenance Support
5.5 Data-Driven Challenges
References
Chapter 6 Fundamental in Artificial Intelligence
6.1 What is Decision-Making?
6.1.1 Importance of Decision-Making
6.1.2 Features or Characteristics of Decision-Making
6.1.3 Principles of Decision-Making
6.2 General Decision-Making Process
6.3 Problem-Solving Process in Industrial Contexts
6.3.1 Six-Step Problem-Solving Model
6.3.1.1 Step One: Identify the Problem
6.3.1.2 Step Two: Determine the Root Cause(s) of the Problem
6.3.1.3 Step Three: Develop Alternative Solutions
6.3.1.4 Step Four: Select a Solution
6.3.1.5 Step Five: Implement the Solution
6.3.1.6 Step Six: Evaluate the Outcome
6.4 System Thinking and Computer Science
6.5 Decision Support Systems
6.6 Data in a Decision-Making Process
6.7 Knowledge Discovery
6.7.1 Approaches to KDP Modelling
6.7.2 The Leading KDP Models
6.8 Business Intelligence
6.8.1 Business Intelligence on a Practical Level
6.8.2 What Does Business Intelligence Do?
6.8.3 Differences Between Artificial Intelligence and Business Intelligence
6.9 Database and Knowledge Base in Decision Support Systems
6.9.1 Differences Between a Database and a Knowledge Base
6.10 Inference Mechanisms in Artificial Intelligence
6.10.1 Deductive Reasoning
6.10.2 Inductive Reasoning
6.10.3 Adductive Reasoning
6.10.4 Case-Based Reasoning
6.10.5 Monotonic Reasoning and Non-Monotonic Reasoning
6.11 Knowledge Interpretation: The Role of Inference
6.11.1 Inference Engine Architecture
6.11.2 Inference Engine Implementations
6.12 From Data to Wisdom
6.12.1 Data, Information, Knowledge, and Wisdom
6.13 AI and Software Engineering
6.13.1 Artificial Intelligence and Software Engineering
6.13.1.1 Aspects of AI
6.13.1.2 Aspects of SE
6.13.2 The Role of Artificial Intelligence in Software Engineering
6.13.3 When Does Artificial Intelligence for Software Engineering Work Well?
6.13.4 Relationship Between Approaches to Artificial Intelligence for Software Engineering
6.13.5 Intersections Between Artificial Intelligence and SE
6.13.5.1 Agent-Oriented Software Engineering
6.13.5.2 Knowledge-Based SE
6.13.5.3 Computational Intelligence and Knowledge Discovery
6.13.5.4 Ambient Intelligence
References
Chapter 7 Systems Thinking and Systems Engineering
7.1 Definition of System
7.1.1 Characteristics of a System
7.1.1.1 System Structure
7.1.1.2 System Stakeholders
7.1.1.3 System Attributes
7.1.1.4 System Boundaries
7.1.1.5 System Needs
7.1.1.6 System Constraints
7.1.2 Systems Engineering
7.1.2.1 Holistic View
7.1.2.2 Interdisciplinary Field
7.1.2.3 Managing Complexity
7.1.2.4 Systems Engineering Processes
7.2 Systems-of-Systems
7.2.1 Manufacturing Supply Chains
7.2.2 Embedded Automotive Systems
7.2.3 Smart Grids
7.3 System of Interest
7.3.1 System of Interest Architectural Elements
7.3.1.1 Personnel System Element
7.3.1.2 Equipment System Element
7.3.1.3 Mission Resources System Element
7.3.1.4 Procedural Data System Element
7.3.1.5 System Reponse Element
7.3.1.6 Facilities System Element
7.4 Enabling Systems
7.5 System Lifecycle
7.5.1 Stages of a System Lifecycle
7.5.1.1 Conception
7.5.1.2 Design and Development
7.5.1.3 Production
7.5.1.4 Utilisation
7.5.1.5 Maintenance and Support
7.5.1.6 Retirement
7.5.1.7 Applications of the Six Lifecycle Stages
7.5.2 Defining Lifecycle Models
7.5.2.1 A Linear Lifecycle Model
7.5.2.2 An Iterative Lifecycle Model
7.5.2.3 An Incremental Lifecycle Model
7.6 Hierarchies
7.7 System Item Structure
7.7.1 Types of Systems
7.7.1.1 Open and Closed Systems
7.7.1.2 Deterministic and Probabilistic Systems
7.7.1.3 Physical and Abstract Systems
7.7.1.4 Man-Made Information Systems
7.7.2 Information Systems in the Organisational Context
7.7.2.1 Formal Information Systems
7.7.3 Informal Information Systems
7.7.4 Computer-Based Information Systems
References
Chapter 8 Software Engineering
8.1 Software Engineering Overview
8.2 From Programming Languages to Software Architecture
8.2.1 High-Level Programming Languages
8.2.2 Abstract Data Types
8.2.3 Software Architecture
8.3 System Software
8.3.1 Functions of System Software
8.3.2 Application Software
8.3.3 Engineering/Scientific Software
8.3.4 Embedded Software
8.3.5 Web Applications
8.3.6 Artificial Intelligence Software
8.4 Software Evolution
8.4.1 Software Evolution Laws
8.4.1.1 Static-Type (S-Type)
8.4.1.2 Practical-Type (P-Type)
8.4.1.3 Embedded-Type (E-Type)
8.5 Paradigms of Software Engineering
8.5.1 Traditional Software Engineering Paradigms
8.5.1.1 Classical Lifecycle Development Paradigms
8.5.1.2 Incremental Development Paradigms
8.5.1.3 Evolutionary Paradigms
8.5.2 Advanced Software Engineering Paradigms
8.5.2.1 Agile Development Paradigm
8.5.2.2 Aspect-Oriented Development Paradigm
8.5.2.3 Cleanroom Development Paradigm
8.5.2.4 Component-Based Development Paradigm
8.6 Software Architecture Models
8.6.1 Layered Architecture
8.6.2 Event-Driven Architecture
8.6.3 Microkernel Architecture
8.6.4 Microservices Architecture
8.6.5 Space-Based Architecture
8.7 Software Systems and Software Engineering Processes
8.7.1 Software System Components
8.7.2 Properties of Software
8.7.3 The Importance of Software Engineering
8.7.4 The Software Engineering Process
8.7.4.1 The Layers of Software Engineering
8.7.4.2 A Generic Framework of the Software Process
8.8 Component-Based Software Engineering
8.8.1 Construction-Based Software Engineering Processes
8.8.2 Characteristics of Component-Based Software Engineering
8.8.3 Evolution of Component-Based Software Engineering
8.8.3.1 Preparations
8.8.3.2 Definitions
8.8.3.3 Progression
8.8.3.4 Expansion
8.8.4 Componentisation
8.9 Software Maintenance Overview
8.9.1 Types of Maintenance
8.9.2 Cost of Maintenance
8.9.3 Maintenance Activities
8.9.4 Software Re-Engineering
8.9.4.1 Reverse Engineering
8.9.4.2 Programme Restructuring
8.9.4.3 Forward Engineering
8.9.5 Component Reusability
8.9.5.1 Reuse Process
8.10 Applications of AI in Classical Software Engineering
8.10.1 AI in The Software Engineering Lifecycle
8.10.1.1 AI in Software Project Planning
8.10.1.2 AI at the Stage of Problem Analysis
8.10.1.3 AI at the Stage of Software Design
8.10.1.4 AI at the Stage of Software Implementation
8.10.1.5 AI at the Stage of Software Testing and Integration
8.10.1.6 AI at the Stage of Software Maintenance
References
Chapter 9 Distributed Computing
9.1 Cloud Computing
9.1.1 Advantages of Cloud Computing
9.1.2 Challenges of Cloud Computing
9.1.3 Relationship of Cloud Computing with Other Technologies
9.1.4 Cloud Computing Service Models
9.1.4.1 Software as a Service
9.1.4.2 Benefits of Software as a Service
9.1.4.3 Platform as a Service
9.1.4.4 Infrastructure as a Service
9.2 Cloud Computing Types
9.2.1 Advantages of Hybrid Clouds
9.2.2 Cloud Computing Architecture
9.3 Fog Computing
9.3.1 Benefits of Fog Computing
9.3.2 Disadvantages of Fog Computing
9.3.3 The Fog Paradigm
9.4 Edge Computing
9.4.1 Advantages of Edge Cloud Computing
9.4.2 How Edge Computing Works
9.4.3 Edge Computing and Internet of Things
9.5 A Comparative Look at Cloud, Fog, and Edge Computing
9.6 Data Storage
9.6.1 Relational Databases
9.6.2 Non-Relational Databases
9.6.3 Other Database Structures
9.6.4 Message Brokers
9.7 Information Management
9.7.1 Centralised Databases
9.7.2 Decentralised Databases
9.7.3 Web Databases
9.7.4 Cloud Database
9.7.5 Data Lakes
9.7.6 Object Storage
9.8 Data Fusion and Integration
9.8.1 Problems with Data Fusion
9.9 Data Quality
9.10 Communication
9.10.1 Machine Learning in Communications
9.10.1.1 Routing in Communication Networks
9.10.1.2 Wireless Communications
9.10.1.3 Security, Privacy, and Communications
9.10.1.4 Smart Services, Smart Infrastructure, and Internet of Things
9.10.1.5 Image and Video Communications
9.11 Cognitive Computing
9.11.1 Theoretical Foundations for Cognitive Computing
9.11.1.1 Cognitive Informatics for Cognitive Computing
9.11.1.2 Neural Informatics for Cognitive Computing
9.11.1.3 Denotational Mathematics for Cognitive Computing
9.11.2 Models of Cognitive Computing
9.11.2.1 Abstract Intelligence Model of Cognitive Computing
9.11.2.2 Computational Intelligence Model of Cognitive Computing
9.11.2.3 Behavioural Model of Cognitive Computing
9.11.3 Applications of Cognitive Computing
9.11.3.1 Autonomous Agent Systems
9.11.3.2 Cognitive Search Engines
9.12 Distributed Ledger
9.12.1 Distributed Ledger and Blockchain
9.12.1.1 How Blockchain Works
9.12.1.2 Types of Blockchain
9.12.1.3 Advantages of Blockchain
9.12.1.4 Uses of Blockchain
9.12.1.5 Limitations of Blockchain
9.12.1.6 Blockchain and Smart Contracts
9.12.1.7 Blockchain Frameworks in Use
9.12.1.8 Bitcoin
9.12.1.9 Ethereum
9.12.1.10 R3 Corda
9.12.1.11 Hyperledger Fabric
9.12.1.12 Comparison of Distributed Ledger Technologies
9.13 Information Security
9.14 Cybersecurity
9.14.1 Challenges and Responses
9.14.2 A Cybersecurity Framework
9.14.3 Access Control Security
9.14.4 Information Transmission Security
9.14.5 Data Storage Security
References
Chapter 10 Case Studies
10.1 Case Study 1 – AI Factory for Railway
10.1.1 AI Factory for Railway
10.1.1.1 Technology Platform (AI Factory)
10.1.1.2 Digital Governance Platform (eGovernance)
10.1.1.3 Communication Platform
10.1.1.4 Coordinating Platform
10.2 Case Study 2 – AI Factory for Mining
10.2.1 AIF/M Concept
10.2.2 Expected Results and Effects
10.2.3 Four Pillars of AIF/M
10.2.3.1 Technology Platform (AI Factory)
10.2.3.2 Digital Governance Platform (eGovernance)
10.2.3.3 Communication Platform
10.2.3.4 Coordinating Platform
10.3 Case Study 3 – AI Factory – Augmented Realtiy and Virtual Reality Services in the Railway
10.3.1 Data Fusion and Integration
10.3.2 Data Modelling and Analysis
10.3.2.1 Point Cloud Pre-Processing
10.3.2.2 Classification
10.3.2.3 Labelling
10.3.2.4 Object Extraction
10.3.2.5 Model Creation
10.3.3 Context Sensing and Adaptation
10.4 Case Study 5 – AI Factory – Cybersecurity Services in the Railway
10.4.1 Railway System Stakeholders
10.4.1.1 Infrastructure Manager
10.4.1.2 Maintainer
10.4.1.3 Passenger Operator
10.4.1.4 Interaction with Other Operators
10.4.1.5 Dependencies with External Stakeholders
10.4.1.6 Interaction in Public Areas
10.4.1.7 Interactions in Operational Environments
References
Chapter 11 AI Factory: A Roadmap for AI Transformation
11.1 What is the AI Factory?
11.1.1 Infrastructure of the AI Factory
11.1.2 Becoming an AI Company
11.2 Mastering AI in Manufacturing: The Three Levels of Competency
11.2.1 AI Competency
11.2.1.1 The Apprentice
11.2.1.2 The Journeyman
11.2.1.3 The Master
11.3 Is AI Ready to Run a Factory?
11.3.1 Cyber-Physical Systems
11.3.1.1 Manufacturing and Cyber-Physical Systems
11.3.2 Artificial Intelligence
11.3.2.1 Industrial AI
11.3.2.2 Machine Learning
11.3.2.3 Why is the Adoption of AI Accelerating?
11.3.2.4 Digital Twins in Industry
11.4 The Data-Driven Approach and AI
11.4.1 Pros of AI and a Data-Driven Approach
11.4.2 Cons of AI and a Data-Driven Approach
11.4.3 Implementing the Data-Driven Approach
11.4.3.1 Governance
11.4.3.2 Business
11.4.3.3 Technology
11.5 Data-Driven Approach in Production: Six Implementation Stages and Failure Factors
11.5.1 Six Implementation Steps
11.5.2 Factors of Failure
11.6 Data-Driven Policymaking
11.6.1 Innovations in Data-Driven Policymaking
11.6.1.1 Use of New Data Sources in Policymaking
11.6.1.2 Co-Creation of Policy
11.6.1.3 Government Policymaking in the Transport Sector
11.7 Sustainability: The Triple Bottom Line
11.7.1 Economic Dimension
11.7.2 Social Dimension
11.7.3 Environmental Dimension
11.7.4 Other Sustainability Dimensions
11.7.5 The Triple Bottom Line Implementation
11.7.6 Measuring Sustainability
References
Chapter 12 In Industrial AI We Believe
12.1 Industrial AI
12.1.1 Industrial AI Versus Other AI Applications
12.1.2 Industrial AI and Levels of Autonomy
12.1.3 Data and Training
12.1.4 Using Trained Industrial AI
12.1.5 Conceptual Framework for Industrial AI
12.1.6 Categories of Industrial AI
12.1.6.1 Product Applications
12.1.6.2 Process Applications
12.1.6.3 Insight Applications
12.1.7 Why Industrial AI?
12.1.8 Challenges and Opportunities
12.1.8.1 Data Availability
12.1.8.2 Data Quality
12.1.8.3 Cybersecurity and Privacy
12.1.9 Industrial AI Application Case Examples
12.1.9.1 Monitoring
12.1.9.2 Optimisation
12.1.9.3 Control
12.1.10 Monitoring, Optimisation, and Control as an AI Maturity Model
12.2 Industrial AI in Action
12.2.1 The Future of Industry: The Self-Optimising Plant
12.3 Applying Industrial AI
12.3.1 Industrial AI Requirements
12.3.2 Industrial AI Solutions Landscape
12.3.2.1 Point Solutions
12.3.2.2 Pre-Trained AI Models and Services
12.3.2.3 Development Platforms
12.3.2.4 Developer Libraries
12.3.2.5 Statistical Packages
12.4 The IMS Architecture for Industrial AI
12.4.1 Data Technology
12.4.2 Analytic Technologies
12.4.3 Platform Technologies
12.4.3.1 Operations Technologies
12.5 Visible and Invisible Issues
12.6 Building the Future with AI
12.6.1 AI in Industry 4.0
12.6.1.1 Predictive Analytics
12.6.1.2 Predictive Maintenance
12.6.2 Industrial Robotics
12.6.2.1 Computer Vision
12.6.2.2 Inventory Management
12.7 We Believe in Industrial AI
References
Index