Predictive Maintenance with AI

The Promise: Predict Failures Before They Happen

The idea behind predictive maintenance is compelling: sensors capture machine data in real time - temperature, vibration, speed, energy consumption. AI models analyze this data and recognize patterns that indicate an impending failure. Maintenance can then be carried out in a planned manner before unplanned downtime occurs.

The Reality: It Requires the Right Conditions

In practice, predictive maintenance is more complex than it appears at first glance. For AI models to make reliable predictions, they need large amounts of data - and high-quality data at that.

This means: sensors must be correctly calibrated, data must be captured in a structured way, and there must be enough historical data to recognize patterns. Without these conditions, AI models deliver inaccurate or incorrect predictions.

“Predictive maintenance is not plug-and-play. It requires the right data foundation, the right sensors, and the right platform.”

- Transaction-Network

When Is Predictive Maintenance Worth It?

Predictive maintenance is most valuable for machines that:

For machines that do not meet these criteria, classic preventive maintenance is often the better choice. The effort for predictive maintenance is only worthwhile when the benefit exceeds the costs.

• **Cause high failure costs** (e.g., in process industries or bottleneck machines)
• **Are critical for production** - every downtime has massive consequences
• **Have many sensors** and continuously deliver data
• **Need frequent maintenance** and cause high maintenance costs

The Role of the Platform

For predictive maintenance to work, it needs a platform that bundles all data, processes it, and makes it available for AI models. This platform must be able to integrate data from different sources, normalize it, and analyze it in real time.

At the same time, the platform must translate the results of the AI models into concrete recommendations for action:

Without this translation, predictive maintenance remains a theoretical concept - with data, but without action.

• Which machine needs maintenance when?
• Which spare parts are needed?
• Which service technician is available?
• How is the customer informed?

Conclusion: Predictive Maintenance Is Not Self-Sustaining

Predictive maintenance can massively reduce downtime - but only if the right conditions are in place. It needs the right data foundation, the right AI models, and the right platform to bundle data and translate it into actions.

Those who meet these prerequisites can achieve real competitive advantages with predictive maintenance. Those who do not should first create the foundations - before investing in AI.