The impact of Artificial Intelligence on the (Industrial) Internet of Things

We have said it before: Data is the foundation of artificial intelligence, and the success of your projects depends on it. Manufacturers often have data stored in many systems, but for sure, an Industrial Internet of Things (IIoT) solution is the one producing data like nothing else in history.

This network of interconnected machinery with sensor technology and software enhances manufacturing and industrial processes. It leverages the power of smart machines and real-time analytics to optimize your production process. However, it does not stop at connecting your machines to the internet (shown as the second stage in the Industrial IoT Maturity Model displayed below).

You need to take advantage of the data of these industrial machines to reach fewer machine failures, reduce scrap and downtime issues, and improve throughput – to name a few.

The power of IIoT data increases with AI

When used in tandem with Artificial Intelligence (AI), IIoT holds a significant potential to reshape our industrial landscape. Artificial Intelligence brings the power of advanced data analytics to the Industrial Internet of Things. By sifting through massive amounts of data generated by IIoT devices, AI can identify patterns that humans simply cannot, enabling unprecedented levels of operational efficiency.

PTC’s Industrial IIoT softwareThingWorx for example, allows you to set up advanced alerts on your production and energy data. You will be informed as soon as a certain data variable passes its threshold. An example of a trigger could be the oil temperature of your device crossing its maximum temperature for more than 15 minutes. However, defining these alerts is still a human activity. By adding AI to it, the system could train itself. It could look for the root cause in case of an abnormal case and define what alert needs to be set up to prevent this from happening in the future.

Source: FIR e. V. at RWTH Aachen University

From reactive, preventive to predictive maintenance

Taking another glance at the Industrial IIoT Maturity Model pictured above, stage 5 mentions “Predictive capacity”. This is where one of the most profound impacts of AI in IIoT lies, in the sphere of predictive maintenance. It allows you to shift away from repairing machinery after a failure (reactive maintenance) or schedule-based maintenance (preventive).

Machine learning algorithms and/or deep learning can anticipate future events based on historical data gathered. The predictive capacity is immensely helpful, whether it's for foreseeing a few seconds ahead to prevent mishaps and damage, or for projecting days or weeks into the future to schedule maintenance of machinery before any breakdown occurs. This way, the technology can minimize downtime and prolong the machinery's lifespan. A similar rule-based system can also evolve over time and adapt to changing conditions and complex datasets.

The same predictive powers can significantly enhance two crucial metrics in manufacturing - workplace safety and energy efficiency. AI-powered predictions can discover safety hazards, preventing accidents before they occur. Through its data analysis capabilities, AI can help industries optimize their energy consumption, resulting in significant cost savings and a greener industrial process.