Hands-On Industrial Internet of Things
Giacomo Veneri Antonio Capasso更新时间：2021-06-10 19:22:49
最新章节：Leave a review - let other readers know what you thinkcoverpage
Title Page
Dedication
About Packt
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Packt.com
Contributors
About the authors
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
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Reviews
Introduction to Industrial IoT
Technical requirements
IoT background
History and definition
IoT enabling factors
IoT use cases
IoT key technologies
What is the I-IoT?
Use cases of the I-IoT
IoT and I-IoT – similarities and differences
IoT analytics and AI
Industry environments and scenarios covered by I-IoT
Summary
Questions
Further reading
Understanding the Industrial Process and Devices
Technical requirements
The industrial process
Automation in the industrial process
Control and measurement systems
Types of industrial processes
Continuous processes
Batch processes
Semi-continuous processes
Discrete processes
The CIM pyramid
CIM pyramid architecture – devices and networks
Level 1 – sensors transducers and actuators
Level 2 – RTU embedded controllers CNCs PLCs and DCSes
Level 3 – SCADA Historian
Level 4 – MES
Level 5 – ERP
CIM networks
The I-IoT data flow
The Industrial IoT data flow in a factory
The edge device
The Industrial IoT data flow in the cloud
Summary
Questions
Further reading
Industrial Data Flow and Devices
Technical requirements
The I-IoT data flow in the factory
Measurements and the actuator chain
Sensors
The converters
Digital to analogical
Analog to digital
Actuators
Controllers
Microcontrollers
Embedded microcontrollers
Microcontrollers with external memory
DSPs
PLCs
Processor module
Input and output (I/O) module
Remote I/O module
Network module
Other modules
DCS
Industrial protocols
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
Questions
Further reading
Implementing the Industrial IoT Data Flow
Discovering OPC
OPC Classic
The data model and retrieving data in 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 I-IoT edge
Features of the edge
The edge gateway
The edge tools
The edge computing
The IoT edge versus the I-IoT edge
The fog versus the I-IoT edge
The edge architecture
The edge gateway
The edge computing
The edge tools
Edge implementations
Azure IoT Edge
Greengrass
Android IoT
Node-RED
Docker edge
Intel IoT Gateway
Edge internet protocols
Implementing the I-IoT data flow
I-IoT data sources and data gathering
PLC
Advantages of the PLC
Disadvantages of the PLC
DCS
SCADA
Advantages of SCADA systems
Disadvantages of SCADA systems
Historians
Advantages of Historians
Disadvantages of Historians
Edge deployment and data flow scenarios
Edge on fieldbus setup
Strengths of the edge on fieldbus setup
Weaknesses of the fieldbus setup
Edge on OPC DCOM
Strengths of the edge in OPC DCOM
Weaknesses of the edge in OPC DCOM
Edge on OPC Proxy
Strengths of the edge on OPC Proxy
Weaknesses of the edge on OPC Proxy
Edge on OPC UA
Strengths of the edge on the OPC UA
Weaknesses of the edge on OPC UA
OPC UA on the controller
Summary
Questions
Further reading
Applying Cybersecurity
What is a DiD strategy?
People
Technology
Operating modes and procedures
The DiD in Industrial Control System (ICS) 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 I-IoT data flow
Securing the edge on 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
Questions
Further reading
Performing an Exercise Based on Industrial Protocols and Standards
Technical requirements
The OPC UA Simulation Server
OPC UA Node.js
Starting an OPC UA sample server
Prosys OPC UA Simulator
Installing the Prosys server
Simulating measures
The edge
Node-RED
Summary
Questions
Further reading
Developing Industrial IoT and Architecture
Technical requirements
Introduction to the I-IoT platform and architectures
OSGi microservice containers and serverless computing
Docker
The standard I-IoT flow
Understanding the time-series technologies
OSIsoft PI
Proficy Historian
Uniformance Process History Database (PHD)
KairosDB
Riak TS (RTS)
Netflix Atlas
InfluxDB
Elasticsearch
Cloud-based TSDBs
OpenTSDB
Asset registry
Data-processing and the analytics platform
EMAs
Advanced analytics
Big data analytics
Cold path and hot path
Summary
Questions
Further reading
Implementing a Custom Industrial IoT Platform
Technical requirements
An open source platform in practice
Data gateway
Mosquitto as MQTT connector
Apache Kafka as a data dispatcher
Kafka Streams as a Rule Engine
Storing time-series data on Apache Cassandra
Apache Cassandra
KairosDB
Installing Apache Cassandra
Installing KairosDB
Installing the Kafka KairosDB plugin
Graphite
Developing our batch analytics with Airflow
Installing Airflow
Developing a KairosDB operator
Implementing our analytics
Other open source technologies for analytics
Building an asset registry to store asset information
Building an asset model with Neo4j
Pro and cons of the proposed platform
Other technologies
RabbitMQ
Redis
Elasticsearch and Kibana
Grafana
Kaa IoT
Eclipse IoT
Other I-IoT data beyond the time-series
Apache HDFS and Hadoop
Apache Presto
Apache Spark
Summary
Questions
Further reading
Understanding Industrial OEM Platforms
Technical requirements
I-IoT OEM platforms
Why do we use I-IoT commercial platforms?
The Predix Platform
Registering to the Predix Platform
Installing prerequisites
Configuring the user authentication and authorization services
Configuring the time-series database
Configuring security
Ingesting our first bit of data
Getting our data
Deploying our first application
Predix Machine
Configuring the Predix developer kit
Predix Edge OS
Predix Asset
The other Predix services
The MindSphere platform
Registering to MindSphere
Working with MindSphere
Other platforms
Summary
Questions
Further reading
Implementing a Cloud Industrial IoT Solution with AWS
Technical requirements
AWS architecture
AWS IoT
Registering for AWS
Installing the AWS client
IoT Core
Setting the policies
Registering a thing
Working with an MQTT client
Storing data
DynamoDB
Using acts in IoT Core
AWS Kinesis
AWS analytics
Lambda analytics
Greengrass
Working with Greengrass
Step 1 – building Greengrass edge
Step 2 – configuring Greengrass
Step 3 – building the OPC UA Connector
Step 4 – deploying the OPC UA Connector
AWS ML SageMaker and Athena
IoT Analytics
Building a channel
Building the pipeline and the data store
Preparing the dataset
QuickSight
Summary
Questions
Further reading
Implementing a Cloud Industrial IoT Solution with Google Cloud
Technical requirements
Google Cloud IoT
Starting with Google Cloud
Installing the GCP SDK
Starting with IoT Core
Building the device registry
Registering a new device
Sending data through MQTT
Bigtable
Cloud Functions
Running the example
GCP for analytics
GCP functions for analytics
Dataflow
BigQuery
Google Cloud Storage
Summary
Questions
Further reading
Performing a Practical Industrial IoT Solution with Azure
Technical requirements
Azure IoT
Registering for Azure IoT
IoT Hub
Registering a new device
Sending data through MQTT
Setting up Data Lake Storage
Azure analytics
Stream Analytics
Testing Stream Analytics
Advanced Stream Analytics
Data Lake Analytics
Custom formatter and reducer with Python R and C#
Scheduling the job
ML Analytics
Building visualizations with Power BI
Time Series Insights (TSI)
Connecting a device with IoT Edge
Azure IoT Edge applied to the industrial sector
Building Azure IoT Edge with OPC UA support
Comparing the platforms
Summary
Questions
Further reading
Understanding Diagnostics Maintenance and Predictive Analytics
Technical requirements
Jupyter
I-IoT analytics
Use cases
The different classes of analytics
Descriptive analytics
KPI monitoring and health monitoring
Condition monitoring
Anomaly detection
Diagnostic analytics
Predictive analytics
Prognostic analytics
Prescriptive analytics
CBM
Production optimization analytics
I-IoT analytics technologies
Rule-based
Model-based
Physics-based
Data-driven
Building I-IoT analytics
Step 0 – problem statement
Step 1 – dataset acquisition
Step 2 – exploratory data analysis (EDA)
Step 3 – building the model
Data-driven versus physics-based model
Step 4 – packaging and deploying
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
Greengrass and FreeRTOS
Azure functions on the edge
Analytics on the controller
Advanced analytics
Open System Architecture (OSA)
Analytics in practice
Anomaly detection
Steps 0 and 1 – problem statement and the dataset
Problem statement
Preparing the environment
Step 2 – EDA
Step 3 – building the model
Extracting the features
Selecting features
Defining the training set against the validation set
Building the algorithm
Step 4 – packaging and deploying
Step 5 – monitoring
Anomaly detection with ML
Step 3 – building the model
Predictive production
Steps 0 and 1 – problem statement and dataset
Step 2 – EDA
Step 3 – building the model
Steps 4 and 5 – packaging deploying and monitoring
Summary
Questions
Further reading
Implementing a Digital Twin – Advanced Analytics
Technical requirements
Advanced analytics and digital twins
Data-driven and physics-based approaches
Advanced technologies
ML
Supervised learning
Unsupervised learning
Reinforcement learning (RL)
DL
TensorFlow
Advanced analytics in practice
Evaluating the RUL of 100 engines
Steps 0 and 1 – problem statement and dataset
Problem statement
Preparing the environment
Step 2 – exploratory data analysis (EDA)
Step 3 – building the model
Extracting the features
Selecting variables
Identifying the training set and the validation set
Defining the model
Step 4 – packaging and deploying
Step 5 – monitoring
Monitoring a wind turbine
Steps 0 1 and 2 – problem statement dataset and exploratory data analysis
Step 3 – building the model
Steps 4 and 5 – packaging and deploying monitoring
Platforms for digital twins
AWS
Predix
GCP
Other platforms
Advanced modeling
Other kinds of I-IoT data
Summary
Questions
Further reading
Deploying Analytics on an IoT Platform
Technical requirements
Working with the Azure ML service
Starting with the Azure ML service
Developing wind turbine digital twins with Azure ML
Developing the model
Building the image of the model
Registering the model
Deploying the model
Testing the model
Cleaning up the resources
Understanding the ML capabilities of the Azure ML service
Building the surrogate model with logistic regression and Scikit-Learn
Building the training model
Preparing the cluster to deploy the training model
Submitting the model to the cluster
IoT Hub integration
Implementing analytics on AWS SageMaker
Evaluating the remaining useful life (RUL) of an engine with SageMaker
Downloading a dataset on S3
Starting the notebook
Working with the dataset
Understanding the implementation of a SageMaker container
Building the container
Training the model locally
Testing the model locally
Publishing the image on AWS cloud
Training the model in AWS SageMaker
Testing the model on AWS SageMaker notebook
Understanding the advanced features of SageMaker
Consuming the model from AWS IoT Core
Understanding the advanced analytics capabilities of GCP
ML Engine
Discovering multi-cloud solutions
PyTorch
Chainer
MXNet
Apache Spark
Summary
Questions
Further reading
Assessment
Chapter 1: Introduction to Industrial IoT
Chapter 2: Understanding the Industrial Process and Devices
Chapter 3: Industrial Data Flow and Devices
Chapter 4: Implementing the Industrial IoT Data Flow
Chapter 5: Applying Cybersecurity
Chapter 6: Performing an Exercise Based on Industrial Protocols and Standards
Chapter 7: Developing Industrial IoT and Architecture
Chapter 8: Implementing a Custom Industrial IoT Platform
Chapter 9: Understanding Industrial OEM Platforms
Chapter 10: Implementing a Cloud Industrial IoT Solution with AWS
Chapter 11: Implementing a Cloud Industrial IoT Solution with Google Cloud
Chapter 12: Performing a Practical Industrial IoT Solution with Azure
Chapter 13: Understanding Diagnostics Maintenance and Predictive Analytics
Chapter 14: Implementing a Digital Twin - Advanced Analytics
Chapter 15: Deploying Analytics on an IoT Platform
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更新时间：2021-06-10 19:22:49