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.