Artificial intelligence (AI) is an imagination-capturing concept. Science fiction writers and filmmakers have been captivated by it for years. Now, it is having rather more tangible impacts.
One area where AI is having particularly significant effects is in the Industrial Internet of Things (IIoT). But why?
A symbiotic relationship
The IoT is fundamentally all about data. It captures information that was previously too costly or complex to collect, consolidates and analyses it so that it can be turned into actionable insight.
And AI is all about data too, in that data is the fuel that powers artificial intelligence. It is only through a steady supply of data that AI tools and algorithms can continue learning from themselves, getting smarter and more refined.
So if the IoT captures vast quantities of data and AI needs to be powered by vast quantities of data, we begin to see how the two might exist in a symbiotic relationship. AI enables the information collected by an IoT ecosystem to be put to really valuable use.
Here are three of the ways in which AI can optimise, enhance and improve IIoT ecosystems:
Industrial downtime can occur for multiple reasons – hardware failure, process bottlenecks (see below), human error, or planned downtime so as to conduct maintenance or routine checks. AI can help minimise all these forms. Analysis of performance metrics enables a proactive, predictive approach to maintenance, maximising the useful lifespans of hardware and enabling the scheduling of maintenance and checks at the most appropriate time. It can also automate manual tasks which might cause errors.
Reducing consumable and energy costs
AI enables organisations to get to the root cause of energy fluctuations faster, so that the organisation can identify and repair faults or leaks, take advantage of off-peak energy prices, and use precisely the right amount of consumables such as oil.
The smoother a production line, the more effective the overall industrial operation. AI can analyse the performance of a production line over time and in granular detail, cross-referencing with other data such as the time of year, staffing levels and even the weather in order to make intelligent decisions as to where bottlenecks are likely to occur. From there, action can be taken to remove said bottlenecks and improve efficiency.
And here are three of the questions you need to be able to answer before you begin an IIoT/AI project:
What data can we access?
As outlined at the start, data must flow through all aspects of an IIoT/AI project, which means your starting point has to be – what data can you harness? It needs to be accurate, it needs to be in adequate volumes for AI to be useful, and you need to be able to access it easily and over time. Relatively simple and cost-effective sensors can often be used to gather truly powerful information.
What experts do we have?
Data scientists and subject matter experts both have a key role to play in the success of IIoT/AI projects, and they need to be able to work well together. Whether you have expertise in-house, or seek consultancy from a third party, both areas need to be covered.
What improvements are we driving towards?
AI and the IIoT can be hugely beneficial, but they can also be complex technologies to implement. It is important to have clear goals from the outset as to what you want to achieve, and an understanding that you may take time to get there. As data-powered endeavours, many AI projects have their greatest impacts only after quite substantial amounts of information have been processed and applied, so you may need to be prepared for a longer timeline than other IT projects.