Book Image

Industrial IoT for Architects and Engineers

By : Joey Bernal, Bharath Sridhar
Book Image

Industrial IoT for Architects and Engineers

By: Joey Bernal, Bharath Sridhar

Overview of this book

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.
Table of Contents (19 chapters)
1
Part 1:An Introduction to Industrial IoT and Moving Toward Industry 4.0
6
Part 2: IoT Integration for Industrial Protocols and Systems
11
Part 3:Building Scalable, Robust, and Secure Solutions

Building the model

Before we get into the heart of using Amazon SageMaker to develop the ML model, we have a little more data engineering to consider. SageMaker contains a good number of built-in algorithms and several pre-trained models – one of which we will use in the example. The Random Cut Forest (RCF) algorithm is an unsupervised learning algorithm that detects anomalies in data points from within a set – that is, data points that diverge from a well-structured data series.

RCF is a good algorithm for looking at time series data and determining spikes in data, or possibly some latency or spikes in a dataset due to production or seasonal issues. Because our current raw data is pretty well structured, assuming the value from our simulator is constant or within slight variations, RCF can analyze this data and determine when data points are outside the given target.

A note about architecture and data science

Data science is a growing and complex field. I...