AI factory : theories, applications and case studies

AI factory theories, applications and case studies

"This book provides insights on how to approach and utilize data science tools, technologies and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI-technologies, and their inter-connections. Description of variou...

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Detalles Bibliográficos
Otros Autores: Karim, Ramin, author (author), Galar, Diego, author, Kumar, Uday (Professor of mechanical engineering), author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2023]
Edición:First edition
Colección:ICT in Asset Management Series
Materias:
Artificial intelligence > Industrial applications.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009784598406719
Tabla de Contenidos:
  • 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.

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