Exploring IIoT and How it Impacts Your Digital Transformation Journey
By leveraging today’s scalable IIoT to collect asset information and meta data across vast distributed systems for use in advanced analytics, IIoT offers a quick return on investment.
Written by: Jeff Runyan
The Industrial Internet of Things (IIoT) is a buzzword commonly found today in business and engineering circles. However, the four-word phrase is so much more than just an acronym to sprinkle into conversation to sound trendy or up to date. IIoT encompasses a vast set of technologies that you should be considering for your business.
What is IIoT?
IIoT is an industry extension of the Internet of Things (IoT), a smart device technology found in everyday home environments. IoT emerged around 1999 as a culmination of various Internet and Ethernet developments, with the late 1990s seeing the growth of machine-to-machine (M2M) communications. IoT focuses on the user experience, while IIoT builds on that in the manufacturing environment, allowing increased efficiency, safety, and scalability.
IIoT is the interconnection of smart sensors, actuators, and control devices for improved monitoring and control of industrial processes. It is intertwined with the 4th Industrial Revolution (Industry 4.0), the 21st century’s push for increasingly automated industrial production and supply processes. The 2002 launch of Amazon Web Services (AWS), the first cloud computing service, greatly facilitated deployment of IIoT platforms. Cloud technology allowed for remotely accessible data storage and analysis.
Since then, improvements to and standardization of communications protocols such as OPC Unified Architecture (UA) protocol and Sparkplug B MQTT have made IIoT platforms much more scalable and efficient. IIoT has gained even more traction since 2010 due to falling hardware costs. IIoT is a quickly growing industry, with the global market estimated to reach more than $920 billion by 2025.
System Architecture with IIoT
A key feature of IIoT is the collection and analysis of data from many sensors in the field. Analysis in big data leverages technologies such as machine learning to process datasets too large and complex for traditional, more human-driven analysis. Big data algorithms allow for processing massive amounts of data near-instantaneously, so analytical patterns and anomalies may be identified in real-time. Businesses can thus make better-informed decisions and respond much faster to situations as they emerge.
With the use of pre-trained models, it’s even possible to predict likelihood of future failures using live data streams, enabling operators and management to mitigate or avoid system failures, minimizing maintenance and other sunk costs.
Given the large-scale data collection, IIoT platforms typically store data in a cloud database and leverage cloud servers for big data computations. However, organizations still do on-premises analysis and even computation at the edge of the network. In edge computing, individual nodes handle select calculations to minimize bandwidth utilization and resource costs at the cloud level while performing big data analysis with vast amounts of data.
Additionally, wireless communications are typically employed in IIoT systems to simplify infrastructure deployment and reconfiguration. Protocols such as OPC UA or MQTT, both based on publisher-subscriber models, facilitate coordination of the numerous high-speed data connections within the web of IIoT devices.
Why use IIoT?
By leveraging IIoT to collect asset information and meta data across vast distributed systems for use in advanced analytics, IIoT offers three main channels of return on investment.
Process improvement
The automation offered by IIoT leads to numerous improvements in the production cycle. By continually tracking machine condition, maintenance checks become more intentional as opposed to being regularly scheduled based on a predetermined time interval, decreasing the labor toll for large facilities. Using advanced analytics, machines can be leveraged in an optimized manner, increasing productivity, decreasing energy consumption, and optimizing profit.
Asset tracking
By placing sensors on mobile assets, companies can track operations such as delivery processes in real time and identify on-time statuses. IIoT sensors can also improve product condition by tracking condition indicators of the products, such as temperature, motion inside vehicles, and much more. In the cloud, the network of tracked mobile assets can be analyzed and optimized as a comprehensive system, leading to overall company benefit.
Preventative maintenance
Continuous data streams allow for more accurate visibility of maintenance needs, but they can also be leveraged to understand future risks. Using advanced analytics, tracked condition indicators can be used to predict future equipment issues and enable operators to mitigate them before they result in potentially costly downtime.
