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Conceptual drawing of IoT and AI

Comment: Are Artificial intelligence and the Internet of Things a match made in heaven?

Image credit: EU Automation

In manufacturing systems, Internet of Things devices can be likened to the nerves that measure parameters and collect data in our bodies. In the same way, artificial intelligence can provide the same functions as our brains, using this information to decide the best response.

Gartner has predicted that, by 2022, more than 80 per cent of enterprise Internet of Things (IoT) projects will include an AI component, soaring from just 10 per cent in 2019. The reasons behind this growth are clear ─ IoT devices generate vast amounts of operational data in industrial facilities, more than we may be able to deal with. Facilities collect information on temperature, pressure, vibration, flow and more, all of which we could glean valuable insights from.

Artificial intelligence (AI), or more specifically machine learning, can simulate intelligent behaviour and learn from experience to make use of sensor data, creating actionable insights from our connected devices. It’s a match made in heaven.

Why does AI offer such benefits to IoT users? Traditional data-analysis techniques weren’t designed with Big Data in mind and can’t efficiently process the vast amounts of real-time data now being collected from machines. AI can process large data sets to identify patterns and insights with minimal or no human intervention, a much simpler approach. To enable this, a growing number of IoT platforms offer AI capabilities, including Google Cloud IoT, Microsoft Azure IoT platform and AWS IoT.

Artificial intelligence can also help manufacturers cope with interoperability issues. Different manufacturers’ operational technology systems may not be designed to communicate with each other, or with a central platform capable of providing an overarching view. Factor legacy equipment into this equation and you have a job on your hands. Gathering all the data into one IT system can be a mammoth task, but AI algorithms can help to train systems to analyse information to make this process easier.

Using AI, data analysis can take place in real time so that machinery can quickly respond to events in an emergency, or it can be used to identify patterns in previous data sets and - using predictive analytics - figure out what’s coming next. Deloitte has found that predictive maintenance can reduce the time required to plan maintenance by 20-50 per cent, increase equipment uptime and availability by 10-20 per cent, and reduce overall maintenance costs by 5-10 per cent. It also means you can predict equipment breakdowns before they occur, and have a replacement part handy when it’s most needed.

We’re also seeing AI implemented into edge devices to create the so-called ‘intelligent edge’. For example, Banner Engineering’s DXM Wireless Gateway Controller uses a machine-learning algorithm to gain insights about the status of machines, by generating a baseline of operation and warning and alarm thresholds.

IoT providers are updating their tools to make it easier for users to use AI at the edge. Microsoft, for example, has launched Azure IoT Edge, a platform that enables low-power devices to perform AI locally while retaining Cloud connection for management and modelling. Amazon’s Greengrass has also been updated to incorporate machine learning capabilities.

One challenge is that significant computing power and capacity is needed to process the data quickly, so networks must be built to be suitable for AI. To do this, businesses can consider Edge and Cloud connectivity, scalability, availability, interoperability, bandwidth and more.

Your nervous system would be nothing without the brain. IoT too requires brainpower to work efficiently and AI is up to the job.

Sophie Hand is UK country manager with automation equipment supplier EU Automation.

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