Predictive Maintenance with AI and IoT – and Why It’s Worth It
Traditional AI still plays a strong role in today’s AI solutions. When we talk about traditional AI, we mean methods that existed before the boom of generative AI—namely machine learning. It also includes modern language models, but it is a much broader and older concept.
Why are machine learning and IoT at the core of predictive maintenance?
In machine learning, data plays a central role. Algorithms build a model from data that automatically learns the characteristic behavior of a process – without humans having to define explicit rules. This is especially valuable in situations where rules are difficult to identify or put into words.
A concrete example: an experienced mechanic can recognize subtle changes in equipment behavior when maintenance is needed. However, they cannot easily transfer these intuitive rules to a new employee. Machine learning makes it possible to transfer this intuition into automated systems – as long as enough data is available.
What data is needed?
The use case determines what data is required. Typical observed variables may include, for example:
- sound
- vibration
- temperature
- electricity consumption
- output quality
Sensors and IoT technologies collect this data and transfer it for analysis. With IoT, the multidimensional data generated by equipment and processes can be easily made available for AI applications.
Business benefits – why is this worthwhile?
- Fewer downtime events → production continues without costly downtime
- Optimized maintenance costs → repairs are performed only when needed
- Better quality and safety → issues are detected early
Every unexpected failure can mean production downtime costing thousands of euros. Predictive maintenance powered by AI is an investment that pays for itself.
Ai4Value’s solutions
Machine learning methods can be applied to a wide variety of forecasting and process optimization needs. Ai4Value has experience in developing machine learning algorithms for many different use cases. Our solutions also include a visual IoT monitor that makes understanding multidimensional data easy. For example, based on the vibration data of a CNC milling machine, an entire day’s machining operations and problem areas can be viewed at a glance.
Get in touch – let’s work together to find the machine learning solutions that deliver the greatest added value for your business.
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Pasi Karhu, CTO
“I work as CTO at Ai4Value since its inception in 2018. However, my acquaintance with AI goes back much further: already in the 1990’s I worked with machine learning algorithms and taught a course on artificial neural networks at Helsinki University. Today, I particularly enjoy working with large language models, but my colleagues know they can approach me with a wide variety of AI questions. AI is a fun topic to work on, as its fast development regularly pushes even the most experienced IT guy to new grounds.”