Hands-On Industrial Internet of Things: Build robust industrial IoT infrastructure by using the cloud

Hands-On Industrial Internet of Things
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Preface
   Who this book is for
   What this book covers
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Part 1:Industrial IoT
1
Introduction to Industrial IoT
   Technical requirements
   IoT background
      History and definition
      IoT enabling factors
   IoT use cases
   IoT key technologies
   What is the IIoT?
      Use cases of the IIoT
      IoT and IIoT – similarities and differences
      IoT analytics, AI, and AIoT
      Industry environments and scenarios covered by IIoT
   Summary
2
Understanding the Industrial Process and Devices
   Technical requirements
   The industrial process
      Automation in the industrial process
      Control and measurement systems
      Types of industrial processes
   The CIM pyramid
      CIM pyramid architecture – devices and networks
      CIM networks
   The IIoT data flow
      The Industrial IoT data flow in a factory
      The edge device
      The Industrial IoT data flow in the cloud
   Summary
3
Industrial Data Flow and Devices
   Technical requirements
   The IIoT data flow in the factory
   Measurements and the actuator chain
      Sensors
      Converters
      Actuators
   Controllers
      Microcontrollers
      PLCs
      Advanced Control
      DCS
   Industrial protocols
      The OSI/ISO model
      Automation networks
      The fieldbus
   Supervisory control and data acquisition (SCADA)
   Historian
   ERP and MES
      The asset model
      ISA-95 equipment entities
      SA-88 extensions
   Summary
4
Implementing the Industrial IoT Data Flow
   Discovering OPC
      OPC Classic
      OPC UA
      The OPC UA information model
      OPC UA sessions
      The OPC UA security model
      The OPC UA data exchange
      OPC UA notifications
   Understanding the IIoT edge
      The IoT edge versus the IIoT edge
      The fog versus the IIoT edge
   The edge architecture
      The edge gateway
      The edge computing
      The edge tools
   Edge implementations
      Azure IoT Edge
      AWS IOT Edge
      Node-RED
   Edge internet protocols
   Implementing the IIoT data flow
      IIoT data sources and data gathering
      PLC
      DCS
      SCADA
      Historians
   Edge deployment and data flow scenarios
      The edge on a fieldbus setup
      The edge on OPC DCOM
      The edge on OPC-Proxy
      The edge on OPC UA
      OPC UA on the controller
   Summary
5
Applying Cybersecurity
   What is a DiD strategy?
      Making people aware
      Understanding technology
      Operating modes and procedures
      The DiD in an industrial control system environment
      Firewalls
   Common control-network-segregation architectures
      Network separation with a single firewall
      A firewall with a DMZ
      A paired firewall with a DMZ
      A firewall with DMZ and VLAN
   Securing the IIoT data flow
      Securing the edge on a fieldbus
      Securing the edge on OPC DCOM
      Securing the edge on OPC Proxy
      Securing the edge on OPC UA
      Securing OPC UA on a controller
   Summary
6
Performing an Exercise Based on Industrial Protocols and Standards
   Technical requirements
   The OPC UA Simulation Server
      OPC UA Server Node.js
      Prosys OPC UA Simulator – optional step
   The edge
      Installing a simple edge client
      The Node-RED edge
      Node-RED
   Summary
Part 2:Industrial IoT Architecture
7
Developing Industrial IoT and Architecture
   Technical requirements
   Introduction to the IIoT platform and architectures
   Microservice, containers, and serverless computing
      Docker
      Microservice-oriented technologies
      Software as a service
   The standard IIoT flow
   Understanding the time-series technologies
      Not only sensors
   Asset registry
   Data processing and the analytics platform
      Excursion monitoring analytics
      Advanced analytics
      Big data analytics
   Hands-on with InfluxDB, Node-RED, and OPC UA
   Using Docker Compose and Docker Build
   Summary
8
Implementing a Custom Industrial IoT Platform
   Technical requirements
   An open source platform in practice
      Mosquitto as a data gateway
      Storing time-series data on InfluxDB
      Connecting InfluxDb and MQTT Data Broker
      Starting and testing the data flow
      Visualizing data using Grafana
      Developing our batch analytics with Airflow
      Developing our online analytics with Airflow
      What’s still missing here?
   Building an asset registry to store asset information
      Building an asset model with Neo4j
   Pros and cons of the proposed platform
   Other technologies
      Other open source technologies for analytics
      Other open source platforms
      Other IIoT data beyond the time series
   Summary
9
Building an AWS Industrial IoT Solution
   Technical requirements
   Why do we use commercial IIoT platforms?
      AWS architecture
      AWS IoT
      Registering with AWS
      Installing the AWS command-line interface client
   IoT Core
      Identifying the AWS MQTT endpoint
      Connecting Node-RED
      Using the AWS SDK
      Greengrass V2
      SiteWise
   Summary
10
Implementing an Industrial IOT Data Flow with AWS
   Technical requirements
   Architecture of the exercise
   AWS IoT Analytics
      SageMaker and Athena
      IoT Analytics
   Storing data
   Visualization
      Workforce users
      Grafana
      About visualization
   Summary
11
Performing a Practical Industrial IoT Solution with Azure
   Technical requirements
   Azure IoT ecosystem
   Registering for Azure IoT
   Azure IoT Hub
      Registering a new device
      Building a custom edge
      Azure IoT Edge
      Device twins
      Monitoring messages sent through IoT Hub Metrics
   Showing messages sent through Service Bus
   Summary
12
Implementing an Industrial IoT Data Flow with Azure
   Technical requirements
   Architecture of the proposed exercise
   Setting the data flow
      Data storage
      Data processing
      Azure Synapse
      Machine Learning analytics
   Building visualizations
      Azure Data Explorer
      Grafana
      Power BI
      IoT Central
      More on Azure Data Explorer
   Comparing the platforms
   Summary
Part 3:Industrial Artificial Intelligence of Things
13
Performing Diagnostic, Maintenance, and Predictive Analytics
   Technical requirements
      Jupyter
   IIoT analytics
   The different classes of analytics
      Descriptive analytics
      Diagnostic analytics
      Predictive analytics
      Prescriptive analytics
   IIoT analytics technologies
      Rule-based analytics
      Model-based analytics
   Building IIoT analytics
      Step 0 – problem statement
      Step 1 – dataset acquisition
      Step 2 – EDA
      Step 3 – building the model
      Step 4 – packaging and deploying (MLOps)
      Step 5 – monitoring
   Understanding the role of the infrastructure
   Deploying analytics
      Streaming versus batch analytics
      Condition-based analytics
      Interactive analytics
      Analytics on the cloud
      Analytics on the edge
      Analytics on the controller
      Advanced analytics
   OSA
   Analytics in practice
      Anomaly detection in practice
      Anomaly detection with unsupervised ML
      Anomaly detection with supervised ML
      Predictive production analytics in practice
      Prescription using language models
   Summary
14
Implementing a Digital Twin – Advanced Analytics
   Technical requirements
   Digital twins
      Digital twins in practice
      Implementing a digital twin in IIoT
   AI and IoT
      ML
      DL
      GenAI
      AGI
   Developing a digital twin
      Preparing the development environment
      Evaluating the RUL of 100 engines
      Monitoring a wind turbine
   Platforms for digital twins
      Digital twins platforms
      Advanced modeling
   Other kinds of IIoT data
   Summary
15
Deploying an Analytics Model
   Technical requirements
   Understanding model-as-a-service
   Developing our first MaaS with MLflow
      Starting MLflow
      Developing our ML model
      Understanding Airflow integration
   Working with the Azure ML service
      Starting with the Azure ML service
      Understanding the Azure ML workspace
      Developing wind turbine digital twins with Azure ML
      Cleaning the resources
      IoT Hub integration
      Azure IoT Edge
   Implementing analytics on Amazon SageMaker
      Accessing SageMaker Studio
      Preparing an S3 bucket to store data
      Developing a digital twin with SageMaker
      Consuming the model from AWS IoT Core
      Understanding the advanced features of SageMaker
   Understanding GCP and multi-cloud solutions
   Summary
Index
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