Industrial IoT for Architects and Engineers E-Book

Joey Bernal

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 512

Veröffentlichungsjahr: 2023

Beschreibung

Go beyond connecting services to understand the unique challenges encountered in industrial environments by building Industrial IoT architectures using AWS

Purchase of the print or kindle book includes a free eBook in the PDF format

Key Features

• Understand the key components of IoT Architecture and how it applies to Industry 4.0
• Walk through extensive examples and solutions across multiple Industries
• Learn how to collect, process, store, and analyse Industrial IoT data

Book Description

When it comes to using the core and managed services available on AWS for making decisions about architectural environments for an enterprise, there are as many challenges as there are advantages. This Industrial IoT book follows the journey of data from the shop floor to the boardroom, identifying goals and aiding in strong architectural decision-making.

You'll begin from the ground up, analyzing environment needs and understanding what is required from the captured data, applying industry standards and conventions throughout the process. This will help you realize why digital integration is crucial and how to approach an Industrial IoT project from a holistic perspective. As you advance, you'll delve into the operational technology realm and consider integration patterns with common industrial protocols for data gathering and analysis with direct connectivity to data through sensors or systems. The book will equip you with the essentials for designing industrial IoT architectures while also covering intelligence at the edge and creating a greater awareness of the role of machine learning and artificial intelligence in overcoming architectural challenges.

By the end of this book, you'll be ready to apply IoT directly to the industry while adapting the concepts covered to implement AWS IoT technologies.

What you will learn

• Discover Industrial IoT best practices and conventions
• Understand how to get started with edge computing
• Define and build IoT solution architectures from scratch
• Use AWS as the core of your solution platform
• Apply advanced analytics and machine learning to your data
• Deploy edge processing to react in near real time to events within your environment

Who this book is for

This book is for architects, engineers, developers, and technical professionals interested in building an edge and cloud-based Internet of Things ecosystem with a focus on industry solutions. Since the focus of this book is specifically on IoT, a solid understanding of core IoT technologies and how they work is necessary to get started. If you are someone with no hands-on experience, but are familiar with the subject, you'll find the use cases useful to learn how architectural decisions are made.

Leseprobe

Industrial IoT for Architects and Engineers

Architecting secure, robust, and scalable industrial IoT solutions with AWS

Joey Bernal

Bharath Sridhar

BIRMINGHAM—MUMBAI

Industrial IoT for Architects and Engineers

Copyright © 2022 Packt Publishing

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To my beautiful wife, Christiane. My soulmate, who supports and encourages me every time I get the hair-brained idea to write another book, and my faithful companion Cooper, the Labradoodle that never lets me forget to take a break and go for a walk.

– Joey Bernal

To my wife, Vaishnavi, my daughter Harini, my brother Bhargav, and my parents for their love, support, and inspiration.

– Bharath Sridhar

Contributors

About the authors

Anthony (Joey) Bernal is a creative technical leader focused on the Internet of Things (IoT) and cloud architecture. He has led the development of two major commercial IoT platforms from conception to general availability. He built and ran an IoT start-up, recognized by both Fast Company and Gartner, with customers in manufacturing, oil and gas, and agriculture. Joey is a hands-on architect with solid experience in development, infrastructure, IoT hardware, and cloud and edge platforms. He is an experienced writer and presenter with leadership skills, flexibility, creativity, and technical know-how, which have led to the delivery of many successful products and projects and a sense of humor to enjoy still doing it.

Bharath Sridhar is a technology evangelist and solution architect with over 12 years of experience in digital transformation through IoT. With a constantly curious and exploratory mindset, he works as an enabler of industry 4.0 implementations for Fortune 500 companies. He loves to operate at the intersection of desirability, viability, and feasibility, working to create utilitarian solutions that people love and businesses get delighted and technologists get excited about. He is passionate about knowledge sharing through storytelling. He believes that books are a gateway to curated journeys of personal discovery, experiences, and enlightened knowledge. In his free time, he dreams about the multiverse – its evolution, challenges, and solutions.

I would like to express my gratitude and thanks to all the teachers who have imparted knowledge during my existence, giving it purpose and direction.

About the reviewer

André Hoettgen has been working in industrial manufacturing for more than 10 years and specializes in cross-functional software and solution development. As an Industrial Internet of Things (IIoT) architect, he builds and manages IT/OT infrastructures, develops strategic solutions, and leads their integration. In the wild IIoT world, he organizes vast amounts of data and processes and harmonizes the most diverse systems. His extensive knowledge in numerous disciplines enables him to meet all stakeholders at their level and deliver cross-industry innovations.

Table of Contents

Preface

Part 1: An Introduction to Industrial IoT and Moving Toward Industry 4.0

1

Welcome to the IoT Revolution

Technical requirements

Industry 4.0 and the digitalization of industry

A very brief history lesson

The first Industrial Revolution

The second Industrial Revolution

The third Industrial Revolution

Moving forward and the fourth Industrial Revolution

How IoT can support Industry 4.0 at scale

Sensor technology

IT versus OT

Industry 4.0 and organizational alignment

You can get there from here

Visibility is everything

Business driving innovation

The convergence – IT, OT, and management working together

Reactive and preventative maintenance

Condition-based maintenance

Predictive maintenance

Approach with caution

Leveraging good architecture to drive progress

Observability

Repeatability lowers cost

Summary

2

Anatomy of an IoT Architecture

Technical requirements

Architectural thinking

Reference models

Architectural components for IoT

Designing in layers

Defining the layers

Bringing it all together

Defining standards

Basic project review recommendations

Setting expectations

Summary

3

In-Situ Environmental Monitoring

Manufacturing in the bigger context

What do we mean by environmental monitoring?

Why is monitoring important?

Integration patterns and protocols

Getting data from the field

Data drives everything – a common use case

OEE for a legacy plastic manufacturer

Adventures of a humble data packet

Wired versus wireless

Wireless network evaluation

What network should you choose?

Wireless gateways versus Intelligent edge devices

Summary

4

Real-World Environmental Monitoring

Technical requirements

Exploring wireless networks and protocols

Standardizing your approach

Data capture architecture

Understanding gateway and network setup

Setting up a TTN IoT gateway

Setting up Milesight UG65

Configuring and deploying sensors

Connecting the AM319 multi-sensor

Collecting data in the cloud

Cloud data ingestion

Data transformation and storage layer

Setting up a basic dashboard

Putting the layers together

Summary

Part 2: IoT Integration for Industrial Protocols and Systems

5

OT and Industrial Control Systems

Technical requirements

Revisiting the basics of manufacturing

Understanding industrial control systems

Exploring PLCs

History and evolution of PLCs

Deconstructing PLCs

PLC programming

PLCs versus microcontrollers

The industrial communication of the future

PROFINET and OP CUA

Single Pair Ethernet

Advanced Physical Layer

Time-Sensitive Networking

Industrial 5G

Mapping your integration strategy

Differentiating OT cybersecurity from IT

Where IT meets OT in industrial systems

Why do we need convergence?

What drives IT/OT convergence?

Revisiting the DIKW model

Summary

6

Enabling Industrial IoT

Technical requirements

The opportunity of Industry 4.0

Integration complexity

Understanding IT and OT convergence

The emergence of the digital twin

Architecture decisions for enablement

AWS services for IoT

Revisiting cloud data ingestion

Data collection options

Intelligence at the edge with AWS IoT GG

Adopting technical innovations

Which industries are leading the way?

Summary

7

PLC Data Acquisition and Analysis

Technical requirements

PLC hardware and architecture

Operating PLCs

PLC programming – ladder diagrams

Practical application of PLCs – a smart mocktail bar

Data integration and mapping

Data ingestion and data analysis

Summary

8

Asset and Condition Monitoring

Technical requirements

Understanding APM and system monitoring

What is APM?

APM at scale

Investigating data processing architecture

Simulating data for processing

Processing data with IoT Analytics

Entering the channel

Managing the data pipeline

Cataloging and querying curated messages

Making meaningful decisions with your data

Summary

Part 3: Building Scalable, Robust, and Secure Solutions

9

Taking It Up a Notch – Scalable, Robust, and Secure Architectures

Technical requirements

Understanding IIoT architectural principles

Exploring IIoT reference architectures and how to use them

The IIRA

RAMI 4.0

Getting to know the AAS

Getting to know the DSB

System considerations for architectural design

AWS reference architecture for IIoT

Leveraging architecture for AI and ML

Edge and cloud reference architecture for ML

Common issues with AI for Industry 4.0

Summary

10

Intelligent Systems at the Edge

Technical requirements

Edge computing and AWS Greengrass

Technology options for your edge approach

Greengrass to the rescue

Setting up our scenario

Modbus data

Greengrass permissions

Communicating between Greengrass components

Modbus Request component

Component deployments

The next component – ModbusTCP

Sending data to the cloud

Summary

11

Remote Monitoring Challenges

Technical requirements

Why do we need remote monitoring?

Long-range data acquisition challenges

Case study 1: remote monitoring of a solar farm

The heavy edge configuration

Central cloud-based monitoring platform

Data communication

Data analytics

Concluding remarks

AWS reference architecture for solar farm remote monitoring

Architecting for sustainability

Case study 2: remote monitoring of a CCS unit

Summary

12

Advanced Analytics and Machine Learning

Technical requirements

ML and industrial IoT

Interference versus rules

Learning from the data

Engineering your data

Retrieving voltage and current data

Decoding the values

Building the model

Transforming training data

Setting up and training our model

Deploying and testing the model

Real-time inference

Summary

Appendix: General Cybersecurity Topics

OT security strategies

Comprehensive security framework

Orchestrating a robust security program

OT cybersecurity areas of concern

OT cybersecurity best practices

Summary

Index

Other Books You May Enjoy

Preface

Anyone who picks up this book already knows something about the Internet of Things (IoT). The term is everywhere, in the news and on social media, with IndustrialIoT (IIoT) quickly becoming the focus for many organizations. But how do you get started? How do you design and build a system that will provide new levels of engagement with your environment? In addition, the level of noise in this field is astounding. Hundreds of vendors and solution providers are trying very hard to explain the right way (their way) of implementing IoT within your environment.

This book provides one such approach, with different options to build an IIoT ecosystem for your environment using Amazon Web Services. We will cut through the hype, getting to the heart of what is needed, why it is necessary, and how to approach your specific objectives. This book looks at designing a robust and scalable IoT platform, starting with the basics and adding capability along the way.

Our goal is to provide theory and hands-on examples for you to learn. We provide the information you need to make thoughtful choices and help your organization grow as you move further toward Industry 4.0. In addition, there are plenty of hands-on examples within these pages. These examples are simple enough that you can understand and implement the example code and scenarios but still complex enough to be considered jump-off points for your journey. This is not your father’s “Hello, World!”

We hope you enjoy reading and learning from this book as much as we did writing it!

Who this book is for

This book is for architects, engineers, developers, and technical professionals interested in building an IoT ecosystem focused on industry solutions. This book will guide you in thinking about architecture and design that provides the integration and capability you need while focusing on good architecture as you go.

As readers, you may come from various industries looking to design and build a solution that can stand up to the rigors of an industrial or extreme environment. For this reason, the examples in this book will use readily available industrial-strength hardware and software so that you can recreate a robust solution that provides immediate value to your organization.

What this book covers

Chapter 1, Welcome to the IoT Revolution, focuses on Industry 4.0 and using data to drive efficiency and optimization. A data-driven mindset will be our why, and IoT will be the how. The goal of this chapter will be to understand some history and theory, think about your business cases, and start to define a solution in such a way that you can derive meaningful results while working to move your industry forward.

Chapter 2, Anatomy of an IoT Architecture, walks you through the decision-making process when designing a cloud application. We aim to help you think like an IT architect. Where does the process begin, and how do we accomplish our business goals from a technical perspective? We will evaluate options and trade-offs as we review the architectural layers within the overall design.

Chapter 3, In-Situ Environmental Monitoring, looks at environmental monitoring solutions more holistically. In this chapter, we will explore several common industries and use cases for environmental data collection. We will focus on the approach and the value we can get from our data, looking at how we can collect measurements from various circumstances outside traditional machine data capture.

Chapter 4, Real-World Environmental Monitoring, provides a look at environmental monitoring solutions, which have slightly different goals than what might be considered traditional industrial monitoring. For example, agriculture and ranching are diverse industries that depend significantly on environmental factors. The oil, gas, mining, and maritime sectors also have potential use cases.

Chapter 5, OT and Industrial Control Systems, considers existing control systems prevalent in the industry, commonly attributed as the OT layer. These are part of layers 0, 1, and 2 of the Purdue model and ISA-95 architecture. This chapter will help us understand real-time manufacturing execution systems and how machines are orchestrated to maximize production.

Chapter 6, Enabling Industrial IoT, logically forms the crucial integration layer for enabling IIoT applications. The objective of this chapter is to dive deep into the complexities and nuances to appreciate the need for such convergence between IT systems and OT systems. While you will be aware of the challenges of integration in a typical manufacturing scenario, you can also take away strategies and ideas to enable their convergence.

Chapter 7, PLC Data Acquisition and Analysis, is a hands-on chapter that will help you understand and appreciate an actual integration of a Programmable Logic Controller (PLC) with a data acquisition system. It starts with the architecture and design of a programmable logic controller and the evolution of hardware from the 1960s. We will also introduce you to programming a PLC with ladder logic and tools from various Original Equipment Manufacturer (OEMs). The critical facet of configuring the protocols, mapping the tags, and retrieving the data from PLCs shows you a fundamental integration point between OT and IT.

Chapter 8, Asset and Condition Monitoring, explores the ability to monitor and assess the performance of your equipment and processes across the factory floor. Unhealthy assets can contribute to various issues across the manufacturing process, from unexpected downtime to reduced productivity and output quality. Our goal is to contribute to efficiency by monitoring, maintaining, and improving each piece of equipment within a process.

Chapter 9, Taking It Up a Notch – Scalable, Robust, and Secure Architectures, looks at architectures fundamental to building a system. This chapter will address the need for an architectural framework to develop scalable, secure, and robust IIoT applications. This chapter will cover the broad spectrum of industry-wide architecture IIoT design considerations, with references from the Industrial Internet Consortium (IIC) and Reference Architecture Model Industrie 4.0.

Chapter 10, Intelligent Systems at the Edge, focuses on edge technology – compute power located within the factory. While we will still need to leverage the cloud and send data for processing and storage, our immediate activity will be more local so that we can be located near our data and the systems with which we interact.

Chapter 11, Remote Monitoring Challenges, are fundamental to autonomous operations. Remote monitoring plays a significant role in business continuity, predicting and mitigating failure scenarios. This chapter will focus on remote situations and decision-making about bandwidth concerns, power consumption, and the volume of data. We will examine different options for data transfer, such as 5G, satellite, and long-range wireless options.

Chapter 12, Advanced Analytics and Machine Learning, provides a comprehensive machine learning example. We explain how to derive an anomaly model based on sample data collected from the edge. Our primary goal is to illustrate the end-to-end, model-driven data engineering processes for our IoT efforts.

To get the most out of this book

We ask that you bring an open mind and have some patience. Not every step within the examples is documented. We expect you to have some technical skills in the cloud, AWS, and solution architecture. Additionally, as we point out in several chapters, if something appears not to be working, it is most likely due to AWS permissions.

Software/hardware covered in the book

Operating system requirements

Python: Knowledge of Python will be helpful. Code examples are in Python but are easy to follow if you are unfamiliar with the language. The code samples are easy to read and edit in your environment.

Much of the hands-on work is done within the AWS console. You will require an AWS account and some familiarity. Most examples work within the free tier; however, in the final chapter, which uses Amazon SageMaker, you should track your cost closely.

Visual Studio Code: This is used in some of the later chapters for all the Python code examples. This includes lambdas and edge components that we create. As an IDE, it is easy to use and free!

All the edge processing examples within the book use the Linux operating system, which may require knowing the basic commands when working on a Linux system.

Edge device: An edge device for installing and running Greengrass will be necessary. A Raspberry Pi will do the trick if you can find one. It should be on the same network as your Modbus simulator or device. This simulator can be a Windows edge device or PC if you are more comfortable with that OS.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Industrial-IoT-for-Architects-and-Engineers. If there's an update to the code, it will be updated in the GitHub repository.

We have code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots and diagrams used in this book. You can download it here: https://packt.link/wi9wN

Conventions used

There are several text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “This WirelessDeviceId is created by AWS when you add the device.”

A block of code is set as follows:

def lambda_handler(event, context):     print("Received event: " + json.dumps(event, indent=2))

Any command-line input or output is written as follows:

$ cd /greengrass/v2/logs/ $ tail -f com.environmentsense.modbus.ModbusRequest.log

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Select System info from the Administration panel.”

Tips or important notes

Appear like this.

Get in touch

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Part 1:An Introduction to Industrial IoT and Moving Toward Industry 4.0

Need help with the basics of the Industrial Internet of Things (IIoT) and looking to learn more about its importance? The first two chapters will provide just that, introducing digital evolution and helping decision-makers realize business values. This section explains IIoT and enterprise architecture’s critical core foundation components, relating them to the AWS cloud. This part of the book will also assist you in getting started with IoT and solution architecture principles.

The second half of this section takes you deeper into wireless technology approaches to IoT. This approach allows us to learn key concepts and build an end-to-end example of collecting data from the field. Architecture is an evolution, and we will explore many IoT concepts by starting here. Additionally, we will take a few baby steps in collecting, processing, storing, and displaying your IoT data.

This part of the book comprises the following chapters:

Chapter 1, Welcome to the IoT RevolutionChapter 2, Anatomy of an IoT ArchitectureChapter 3, In-Situ Environmental MonitoringChapter 4, Real-World Environmental Monitoring

1

Welcome to the IoT Revolution

This book is designed for architects and industrial engineers looking for guidance in moving into IoT or the Industry 4.0 space, offering some ideas, approaches, goals, and advice to help make your way forward a little easier and more successful. For readers new to IT architecture or the IoT space, we aim to help answer many of those initial questions or at least guide you in asking the right questions. We want to set the stage in these initial chapters before we get too deep into the technical details. Anyone new to these topics should benefit from these initial chapters, especially non-technical stakeholders who want to understand the why and how of Industrial IoT. With this in mind, please consider that we are providing historical and architectural background, guidance, and some best practices, from an IT and system development approach. If things get too technical, you have been warned.

In this chapter, we want to set the stage for understanding Industry 4.0 and help you to understand where it is headed and why it is important. We are going to review why the current Industrial Revolution and Industry 4.0 are so important, know where we are in the current state of technology, and learn how you can build your vision and value statement for driving technologies such as IoT into your organization.

We are going to cover the following main topics:

Industry 4.0 and the digitalization of industryHow IoT can support Industry 4.0 at scaleThe convergence – IT, OT, and management working together Leveraging good architecture to drive progress

In future chapters, we will delve much deeper into the how of Industrial IoT and learn how to implement and use some of this exciting technology, but stay with us. We need to understand some of the history better and discover where it all started. We have also chosen Amazon Web Services (AWS) as the hyperscaler of choice to base our practical examples. AWS is a formidable player in this space and has a great product roadmap and vision associated with Industrial IoT. There will be more about this as we progress across chapters.

Technical requirements

There are no specific technical requirements for this chapter. Readers at every level should clearly understand it. Our focus is setting the groundwork for why Industrial IoT is poised as one of the next major turns of the technology crank and how you can move forward with adoption within your industry.

Industry 4.0 and the digitalization of industry

Many software architects are sometimes wary of the hype around new technology. Great ideas and visions are pivots that lead us into the future and guide us in taking advantage of new technology in both our business and personal lives. However, the road to the current state of technology is paved with great ideas that never made it out of the concept phase, and overly aggressive marketing and sales around new (good and bad) technologies have made everyone just a little more cautious.

Usually, at the early stages of some technologies, marketing and sales teams jump in and take over, looking for any opportunity to push an idea or build a prototype with any potential customer, attempting to work together with customers to build a vision of what the future could be. But then comes the hard work of architecture, design, prototyping, rollout, testing, production, and support. Sometimes, the state of the technology isn’t quite ready, and reality intervenes. If you have been burned enough times, it gets harder to reach back in.

Fortunately for us, Industry 4.0 has made it well past the starting gate and into the reality of many organizations. Even though it has been making progress for most of the last decade, there is still a fair amount of work to be done before it can be considered mainstream technology in many organizations. The evolution and improvements in hardware, such as sensors and processors, software protocols, and integration tools, make retrieving real-time or near real-time data from almost any device or area more accessible and safer. The why of data capture and Industrial IoT is what we will be discussing in this chapter, while the how will be discussed in the rest of this book.

Industry 4.0, or the fourth Industrial Revolution, is commonly thought of as the automation and digitalization of industry and manufacturing systems. IoT and cloud technologies have become critical enablers of this effort and provide the ability to integrate and automate machinery to become more intelligent and adaptive. Ideally, this includes adopting artificial intelligence and machine learning to enable systems to self-monitor and diagnose or predict problems that may occur.

This description does provide a bit of futuristic vision, connotating a kind of rise of the machines approach, but it gives us a good starting point on which to base our discussion.

A very brief history lesson

History books and most university classes on this topic will agree that the world has undergone three previous industrial revolutions. For us, how we got to where we are is maybe not as important as where we are going, so we won’t belabor the history here, but we’ll provide some background to aid in your organizational discussions and help us pinpoint the reason for and the focus of this book.

The first Industrial Revolution

The first Industrial Revolution occurred in the late 1700s when mechanization based on water or steam power began. Traditional thought placed this beginning in the 1780s when the first mechanical loom was designed and built. While (relatively) easy to make, replicate, and ship, this allowed for the first major transition from production using hands to allowing machine-based tools to do the work.

Early industrial progress

There are, of course, precursors to the first Industrial Revolution. Recently, on a trip to the Netherlands, I was able to tour some windmills that advanced industry in the region as early as the 1600s, providing improvements to industries such as milling, weaving, and lumber production. Although windmill technology had been in service moving water in the region for centuries before this, this small evolution in leveraging the technology for other types of work allowed the Netherlands to advance into a new era, most notably in shipbuilding. Unfortunately, the technology could not be as easily exported since wind-driven machines were primarily a defining factor of the region. However, the inventiveness of the Dutch and the innovative use of gears, levers, and screws helped build the groundwork for future industry advances, evolving from, for example, farm animals for drawing water or agriculture.

The fact that much of the work was driven by steam was also important. The steam engine’s efficiency had greatly improved by this time, and it was now lighter and more transportable. Coal, and the ability to mine coal in significant quantities, was essential for powering these steam engines. Adapting these same engines moving in one direction or performing one motion to a different degree of movement allowed for more flexibility and complexity in industrial use. The loom was prominent in this phase because the textile industry was labor-intensive, and it became one of the first industries to adopt and see the benefit of new technology.

The second Industrial Revolution

The second Industrial Revolution often referred to as the technological revolution, started in the late 1800s and was a strong driver for the modern world we live in today. The expansion of almost everything we know and use in today’s world started during this period. Beginning with the growth of railroads and telegraphs, industry expanded further, bringing gas, water, sewer, and electricity and increasing globalization toward the end of the colonial age.

The expansion of electricity and assembly/production lines happened within this period. History credits Henry Ford for inventing the assembly line in 1913, paving the way for advanced mass production. Ford is also credited for advancements with the combustion engine, steel, and new fuels and materials that drove this exciting period of change and once again transformed many industries.

The third Industrial Revolution

The timelines are a little intertwined because advancements were frequently made that lent toward each distinct phase of technological evolution. These revolutions can seem almost continuous if traced from end to end with enough detail and advancements. There have always been significant breakthroughs that highlight the end of the last and the beginning of the next phase of advancement. The third Industrial Revolution started in the late 1900s and is called the Digital Revolution. This registers as a shift from analog technology to digital technology. The invention of the internet and smaller computing technologies allowed us to enter the information age.

The invention of the transistor in 1947 is a critical starting point for this era. However, it was several decades before this technology was adapted enough to be helpful on a large scale, with the ability to design and create integrated circuits consisting of hundreds of transistors. Eventually, this allowed the creation of the single-chip microprocessor in 1971 by Intel, allowing for desktop computers to become readily available.

Moving forward and the fourth Industrial Revolution

Hopefully, this short history lesson about the previous three industrial revolutions has helped you understand where we started and assisted you in visualizing how the technology crank continuously turns. Before you know it, advancement has occurred. In addition, each revolution has added tremendous value, advancing civilization, increasing productivity and safety, and moving the entire world another step forward.

The fourth Industrial Revolution should have no less lofty goals, with even more of a potential impact on civilization as a whole. I admit this sounds a bit too rosy, but think about it in terms of the effects on humanity and the world we live in. Efficiency itself means less waste, less use of energy, and potentially less pollution and impact on the environment. That, in itself, should make an effort to move forward worthwhile, and that these improvements can help increase productivity, quality, and revenue is icing on the cake.

Keep this in mind as you delve through this book and determine how to apply some of the ideas to your industry. The immediate goal may be to save or make more money; however, inside, you should know that you are hopefully doing your small part to help save the world.

Achieving the vision of Industry 4.0 requires effort and time and cannot be completed all in one go. This is especially true for legacy or brownfield industrial operations that have sometimes been in service for decades. Additionally, some industries produce widely varied results based on external conditions, such as farming.

Earlier in this chapter, we looked at the standard definition of Industry 4.0. It is a visionary statement, and there are many companies along the path to achieving that vision. However, many companies are just getting started or thinking about how to get started. Industry 4.0 is about data and the management of that data. Alongside data comes the necessary analysis, information, knowledge, and the innate ability to improve by looking at the right things. Industry 4.0 allows us to go beyond the decades-long approach of the status quo. We know from history that at every phase of change, in probably every sector, many felt that change was not required or too fast.

How IoT can support Industry 4.0 at scale

The authors of this book have spent a lot of time working in IoT, going back well over 10-12 years from when IoT was little more than a buzzword. When we think about IoT, our minds go to cheap, easy-to-use hardware and connected appliances or watches. This new crop of inexpensive hardware has opened people’s eyes to what could be done for minimal cost, but for industry, a different level of hardware is often required.

We can use IoT hardware and software to accomplish the goals of Industry 4.0 by providing a robust and industrial-strength set of technologies that allow for the instrumentation and measurement of equipment and its environment. Bear in mind that industry is often conducted in extreme environmental conditions and the cheapest approach is often not the right approach. A trade-off between cost and reliability should be considered since if you have to replace a component too often, then the value can be lost in effort, time, or the loss of data while waiting for the switch to occur.

Let’s talk about the key areas to be considered when turning to IoT as part of the solution. Let’s say we are going to place a simple sensor on or near a device to measure temperature. We don’t need to be specific at the moment, but just consider the conditions that you might be facing, such as the following:

Tough: Can your sensors and equipment withstand environmental conditions and pressures? Industrial equipment in the field can be in a rough environment. Does the sensor and corresponding transmitter require an ingress protection (IP) or National Electronic Manufacturers Association (NEMA) enclosure rating for protection? IP ratings provide a rating for your enclosure for protection against access to the internal components and protection from the ingress of liquids and dust or dirt, which is essential for harsh outdoor environments. An IP67 rating indicates a solid enclosure that is protected from the ingress of dust and protects against temporary immersion in water up to a few centimeters. NEMA ratings are the same as IP ratings but provide additional classifications against corrosion and hazardous locations. For some environments, such as oil and gas, a NEMA Class I or Class II enclosure is required due to the presence of corrosive liquids, flammable gasses or vapors, or combustible dust. These environmental conditions and requirements can add additional costs and time to your effort in sourcing, testing, and possibly certifying your components for use in the field.Easy to deploy (and maintain): Make it as simple as possible to ensure speed and accuracy when deploying equipment. This ensures that deploying and registering your sensors and equipment is simple, almost bulletproof, for the engineer on site. When deploying a sensor to a piece of equipment or a location, we have to ensure that once the sensor is in place and operating, we can tie it back to the right location. Without that, the effort is useless, and none of the data further up the chain will be reliable. There are several options here. Mobile apps with barcodes and even manual configuration are fine as long as the setup can be done correctly and consistently. Additionally, the sensor should be easy to attach and place. OK, simple is not always possible, but as much pre-configuration as possible should be considered, leaving the engineer to do as little as possible on-site to complete the setup and installation. Runbooks should be well-defined and include any troubleshooting information that might be needed in the field. This is especially true if the deployment people are not experienced in the new technology.Scalable: The ability to quickly deploy many sensors in the field should be considered. This can mean dozens, hundreds, or thousands of sensors across multiple locations or across the globe. Both hardware and software can be a concern when thinking about scalability. Something easy to deploy and configure can be deployed by the thousands; however, if the software or storage is not configured to manage the data, it may result in wasted effort. Cloud technology will help with the software part, although the application requirements to view and analyze data need to be able to keep up as the system grows. This means data systems and analysis should be designed to accommodate the potential millions of readings you might expect from all those sensors. Reliable: This ensures sensors and monitoring will keep working over a long period of time. This is not the same as a sensor or node being tough. It’s about reliability. Reliability is much more important because now we are talking about the electronics, rather than the casing and packaging of the node. Do the electronics have sealed or glued connections? Are the sensors potted or otherwise protected? Potting means filling or surrounding the electronics with some type of gel or epoxy resin, essentially encapsulating the electronics to minimize the dust, vibration, or liquids that affect them. Of course, before going to this extreme, quality control and using high-quality components are recommended. Hot glue on your connections can be the first line of defense against the loosening of wires. If you go to the extreme of potting your components, be aware that it cannot be undone, so when a part goes bad, a replacement for the entire assembly will be needed, which may be costly. Carry out a cost-benefit analysis of the best approach based on your industry to make the right design decisions. Secure: Make sure data and systems are protected from malicious actors or data theft by unauthorized parties. We will talk about security throughout this book. Using IoT technology can potentially leave security holes across the entire data stream. There are several aspects here to consider. Ensure that data is secure while traveling upstream, and protect endpoints so that fake data cannot be introduced into the system to influence results or actions. Since we are talking about the endpoints in sensors or nodes, physical security is the first step to consider here. Do you need to apply any physical security to the deployment location? And if not, what type of tamper monitoring can be put in place to alert you if something seems amiss? Once data hits the cloud, traditional IT security methods can come into play, but with equipment in the field, your system can encounter many different types of threats.

If you are in the industry already, you will already know some of this; if you are an operator, you will know your environment intimately. But it is still worth considering the environment and defining some basic requirements alongside your goals. This is an excellent point to think about the domain you are considering and create a simple checklist with critical criteria that you can build on as we go forward. These considerations go hand in hand with each other and drive toward a common goal. Using the preceding criteria, along with more to be added as we go, can help you navigate decisions and communicate to others the expectations of the technology.

Sensor technology

We use the term sensor technology broadly to include both the sensor or sensors involved in instrumenting an environment, but also the sensor node that reads the data from the sensor and transmits it to the receiver. We separate these currently because they do not always go hand in hand. Sensor technology is constantly evolving and transmission protocols, such as low-power wide-area network (LPWAN) and 5G, continue to evolve. The sensors you wish to use may have specific characteristics and may not always be compatible with a sensor node for transmitting data on the protocol that you have defined. Let me share an example.

Several years ago, during the Wild West of IoT evolution (just kidding, it’s still the Wild West), one of the authors was involved with a proposal for a large city in Nevada; you can probably guess which city. Our partner in the deal was an IoT start-up; actually, it was way more than a start-up, with millions in funding, but it was relatively new to the IoT space. The company had some fascinating communication technology, which should have been a strong competitor to cellular technology and most LPWAN technologies. It was low power and could send data over very long distances, outdistancing other technologies such as LoRa (from long range) by miles.

This company spent a lot of money and energy on trying to sell and further develop its network, and since fewer towers or hotspots were required to blanket an area, they felt they could cover large areas, such as a city, with relative ease and lower cost. While this was probably true, little regard was given to the fact that no sensors or sensor nodes were available to use on the network. Chips were available and provided by the network provider, but the cost, time, and effort were left to sensor vendors to implement. Essentially the sensor vendor or consultant had to make a bet on this working based on little more than faith in the network company. In hindsight, it’s a bet we are glad we didn’t take.

Unfortunately, this turned out to be a failed strategy since the investment was too significant and complex compared to more available options at the time, using protocols such as LoRa, Sigfox, and LTE. I’m still disappointed the company didn’t have the vision to see this hole in their strategy, and they have moved into the realm of also-rans in the IoT space.

The key takeaway here is to keep in mind the following:

Can your sensor node or transmission unit communicate back to the cloud with your chosen protocol, or set of protocols if you need redundancy?Can your sensor node communicate with your actual sensor or set of sensors to read the measurements for data transmission?

There can often be a mismatch here as the sensors themselves can use all kinds of unique protocols. For example, SDI-12 is a standard serial communications protocol used in agriculture and weather sensors and can be challenging to read if the sensor node is not designed for it. The protocol was defined in the late 80s and transmitted ASCII characters over a single data connection. There are many examples of serial protocols in place for industrial systems that can be decades old but are still very much the standard.

Another example is if you need calibrated sensors, such as temperature sensors, that must follow NIST standards to ensure the results adhere to standards. If calibrated temperature sensors using your defined communication protocols are not commercially available, then you have limited options.

Every day it seems, the sensor world gets a little bit brighter as a vast array of sensors, edge devices, and transmitter units or nodes become more readily available. Many of the most popular protocols are available, with new ones coming on slowly as new network technology is better adopted by the community and becomes more readily available. However, there are still sensor solution gaps for many situations.

One of our favorite options for this problem is from a company called Libelium out of Zaragoza, Spain. Libelium offers a robust mix-and-match approach to sensors and communication options of all different types. For example, you can choose sensors for measuring air quality, water quality, security, and agriculture or for integration into industrial protocols such as Modbus. You can pick a communication protocol to connect the sensors and send measurement data to an existing application or web service. Protocols include using anything from LoRa to Wi-Fi to 4G. This flexible approach makes it easy when you try to adhere to a standard communication protocol but cannot find an appropriate sensor that works with your chosen standard.

Cost can certainly be a factor, especially at scale, and while prices seem to be continuously going down, here is where the myth of IoT, again, seems to get in the way of Industry 4.0. You get what you pay for, and this can be crucial in harsh environmental conditions and areas where you need to provide a standard approach.

IT versus OT

There is still a lot of confusion around information technology (IT), operational technology (OT), and this idea of convergence. But essentially, it is a simple concept to understand.

IT is something we are all familiar with in our daily lives. We run applications on our phones or laptops. Many of these applications run on servers or in the cloud and process data-producing orders, sales, and directives or provide some type of analysis. This is the IT world that we know today. It’s a reasonably open world, and access can be gained from anywhere (provided security concerns are followed).

OT, especially legacy systems, can be considered a more closed environment. OT ends within the walls of the factory. When you think of OT, think of supervisory control and data acquisition (SCADA), which is also run by servers but interacts with devices within a defined area of control. At a large scale, consider a power plant or water treatment plant. The pump shuts off when the water in a tank gets too high. Too low, and it starts back up again. Monitors and alerts allow operators to visualize and help manage what is taking place with appropriate alarms and controls.

Industry 4.0 and organizational alignment

Figure 1.1 illustrates how different areas within the business fit together and into the big picture. In order to work, there is a strong dependency across IT, operations, and business and management; all stakeholders must work together to realize the benefit.

Figure 1.1 – Industry 4.0 organizational alignment

OK, what is the big deal? The big deal is that IT’s primary goal is to provide the business and management with information and the ability to support the decisions and operations of the company. How many widgets were produced? Or how many barrels were processed? How many were sold, and at what price? The business lives and dies on this information and data being available faster and more accurately to provide a competitive edge. Often what is missing is an insight into the real-time production of widgets or processing of barrels. Newly built or upgraded factories can provide real-time information, but in legacy systems, even relatively young ones, that information is hidden. And in production, modifying (reverse engineering) devices and machines voids warranty and, if not done correctly, may lead to complications. With the emergence of IoT, we can bring some of that data from the closed OT world into the often more integrated IT world, where it can be used more effectively.

The focus of this book is on getting the hidden data, storing and processing it, and then using this information effectively.

The business is not the only one to benefit from introducing new data. Operational teams will gain insight into the equipment and production that they didn’t have before. Uptime and maintenance can be improved, cost reduced, and throughput increased as a new understanding, and a new normal of the environment begins to emerge. The full benefit of digitalization should become clear in the rest of this chapter and throughout this book as we share examples of collecting data and then using that data to realize value across the organizational spectrum.

You can get there from here

Industry 4.0 is driven by IoT, but it is just one part of the picture. A big part, granted, as it allows visibility into equipment and operations as never before. A longer roadmap is required to achieve the vision of digitizing your industry and the transformative changes that can take place.

Important note

We are not a fan of big, complicated, eye-chart-type visuals, so throughout this book, we will keep the visuals simple and coherent to allow you, the reader, to immediately understand the concept rather than asking you to try and understand something overly complex.

Figure 1.2 illustrates a basic roadmap toward digitizing your industry or moving toward Industry 4.0. We have broken this down into four primary areas of consideration for improvement. Within each of these areas, there is a vast number of considerations both on the technical and business side to consider.

For many, the status quo or current state of their process is operate. Consider this business as usual, and maybe decades-old processes that, for the most part, just work. There may be some instrumentation, perhaps even a lot, but no cohesion or integration across machines, systems, or plants. Everyone knows we can do better, but how do we move forward? Figure 1.2 illustrates a set of steps for continuous improvement in your equipment and environment. Instrumentation and acting on it improves both the business and technical responses of the organization.

Figure 1.2 – Industry 4.0 roadmap

Let’s talk about each step of the process outlined in turn. We have labeled each area based on the technical changes because that is the focus of this book. However, this can be adapted based on your principal needs.

Instrument and connect

Moving beyond the general operate state requires in-depth visibility of your systems and environment. Consider this the instrument phase, where the goal is to start to gather data from your systems and environment. The other side of the equation is to know what should be instrumented and why. This effort of instrumentation and collecting measurements is where business and operations can collaborate to ensure that the data collected is needed and understand how that data will be used to drive processes and the business forward.

It is usually not the best strategy to jump in and instrument everything. While it may seem like more is better and that you have nothing to lose by doing so, spending time and money on equipment, manpower, bandwidth, and storage for data that is never used ends up being a losing proposition. Once committed, it may require ongoing maintenance for data that does not provide good value.

Another question to ask is how much data is needed. This depends on the velocity of what you are measuring and collecting. Some systems can churn out hundreds of measurements per second. How and where should this information be stored and analyzed? Does all of it need to go to the cloud? Can we process this on the edge and provide aggregate results? What are the pros and cons of taking an approach toward managing this data? Business and operations should be involved intimately in these discussions to help drive what level of granularity is needed and how it will be used. This in turn can drive IT decisions for data management and processing.

Baseline and analyze

Baselining your system’s normal operating environment can be an eye-opening experience. Sometimes (actually a lot of the time) we don’t really know what normal is for our equipment until we measure it and then see it in some graphical format. SCADA systems often have this insight into pressure, temperature, and flow characteristics, but not all industrial operations are driven by SCADA, or the information is hidden from all but on-site equipment operators. The insight gained here can be enormous. Measuring a handful of values can provide deeper information about the working condition of a piece of equipment or an end-to-end system and, as we will see later, drive efficiency and potential maintenance issues. Understanding the baseline of system performance and conditions at a known production rate can be powerful, as well as asking questions such as, what happens when the production rate goes up, and how does that affect the machine conditions?

Defining a baseline can take a long time; it is not done in a day or even a week. Expect at least several production cycles, which could be seasonal-based activities that could take months or years. Hopefully, most cycles do not take that long, but if your industry is influenced by weather or environmental conditions, there is that possibility. You can continuously gain good insight by getting comfortable with what your baseline looks like along the way, but unexpected curves and influences only occur with time.

Prediction and alerting

From a technical side, we have many opportunities. Now that systems are being more closely monitored, you should expect to see variations in the data as problems occur, and equipment shuts down. Maybe there were some unexpected vibrations or a temperature rise before it occurred. Can we monitor for a particular set of variances? Do the vibrations occur when a part is ready for replacement or maintenance? This is the beginning of condition-based maintenance, where new data or real-time monitoring of the environment can alert the operator to a possible set of conditions that may fail.

To accomplish this, we need to start to build predictive models. Tooling today can make it a relatively straightforward process to create a predictive model; however, much of the work in the baseline phase will help you determine which data to prepare for modeling. Generally, we are looking at data to help predict downtime or failures of equipment or systems; however, does the data do this? We will dive into some details about predictive modeling and how to use this in your architecture in the coming chapters.

We can often start our journey by using simple thresholds or comparisons on specific values or sets. This is especially true when you know what specific events or conditions you are looking for but are not quite sure how predictive models will advance your cause. Does the temperature rise above a specific degree? Does the energy usage on a pump get higher while the pressure gets lower? These are simple examples, granted, but powerful tools in helping to determine when something might need to be checked. At this point, we are still triggering more manual alerts, effectively telling someone to check something. This could be as simple as an email or SMS, or a more advanced trouble ticket being opened automatically on your enterprise asset management system.

But really, we can now take this further into the business side of things and better understand production cycles and issues and capacity constraints, not only of finished products but subprocesses that may cause bottlenecks.

Automation and improvement

Industry 2.0 and 3.0 brought a lot of automation into manufacturing and processing. Our focus is more on the automation of the overall business and what is produced. The ability to monitor and eventually steer your production closer to real-time allows the business to be more agile and respond more easily to customer demands. This is a topic well beyond the scope of this book, and would possibly include connecting customer demand, supply, and fulfillment, as well as the digitalized production or factory that is our focus here.

However, with a deeper analysis of your historical data over time, a more detailed analysis can occur of where and when improvements can be made.

Visibility is everything

We probably can’t say this enough. Possibly, this is the gist of the entire book, along with some focus on what to do after you have better visibility. It was mentioned before that understanding your baseline, or the normal operating conditions of your environment can provide clear insight into what is truly normal and when some type of abnormality occurs. This can only happen with clear instrumentation. This is true in almost any industry or science. Most experts will explain that the instrumentation of your environment allows you to gain new insight with a precision not previously available. Software developers who have used deeper inspection, such as bytecode instrumentation or injection, can easily explain the advantage of increased visibility into aspects of a running system. The same is true for physical systems and being able to view and analyze the physical characteristics of a piece of equipment or an environment.

Another aspect to consider is global or widely distributed operations. Modern equipment or systems can be outfitted with all the instrumentation needed for the safe and efficient running of the process. However, what about systems that are geographically distributed across vast areas? Combining and even comparing information from systems globally can provide new opportunities for decision-making.

Along this path should be a feedback loop, allowing adjustments and updates across the entire monitoring chain.

Business driving innovation

A quick web search will provide an abundance of IoT information, specific lists of ideas, examples, or use cases for implementing IoT for your industry, and the value it provides. Sometimes there are interesting use cases, but often it is driven by marketing and sales looking to sell their solution. Unless you have a good working knowledge of an industry, this can be misleading. Earlier, we mentioned that just because you can instrument something does not always mean you should. Time and cost considerations should come into account. Consider the cost of adding sensors to gather information, but also the data collection and maintenance costs of continuing to gather data.

IT, operations, and business stakeholders need to work together to understand what it is that they want to achieve. Then real subject matter experts need to be involved in telling you how to get it and then interpret the right data points to achieve those goals. Operations understand better than anyone how to develop, manufacture, or produce materials or goods. Business stakeholders know how to price, sell, and distribute those goods to end customers. There are nuances in business and operations that the other may not understand intimately or agree with, but working together to achieve better visibility and control can be a powerful weapon for competing on the global market.

The truth is, business and management may not know what they need to instrument at a detailed level. But they do know what information they need to make decisions, such as better overall equipment effectiveness (OEE), downtime reports, or more detailed forecasting for service lines over a period of time. OEE is a process for measuring manufacturing productivity by looking at equipment availability and performance, and the quality of manufacturing output. Operations can then make an informed decision about what they need to do to obtain and provide that information. It’s a complex process that is greatly oversimplified here, but hopefully provides some guidance that no one area of the business should work in a vacuum on this endeavor.

So far, we have provided a big-picture overview of Industry 4.0, the digitalization of the industry, and Industrial IoT. There are multiple approaches to accomplishing systematic improvement in your production and management of equipment and processes, and the roadmap is one approach. Moving forward, we want to dig deeper into some of the technical aspects of starting your journey and adopting a digitalization mindset and approach. What are the steps and goals for moving forward and getting incremental value along the way? In addition, what are the pitfalls in adoption and understanding how difficult it will be? We will be exploring more of the idea of instrumentation, analysis, and convergence for providing value across all stakeholders.

The convergence – IT, OT, and management working together

Rarely does an opportunity come around in the industry for all aspects of the organization to come together for everyone’s benefit. Industry 4.0 allows that to happen. The digitalization of industry can benefit all aspects, allowing the business to make better decisions around schedule, price, and volume and providing operations with better tools to make decisions about maintenance, downtime, and upgrades of equipment within a plant or factory.

Evolving