Predictive maintenance with AI: from sensor data to a well-founded maintenance decision

What predictive maintenance with AI actually is

Predictive maintenance is a form of condition-based maintenance: instead of scheduling maintenance at fixed intervals (preventive) or only reacting when something breaks (corrective), the current state of a machine is monitored through measurements such as vibration, temperature, power consumption, pressure or sound. Machine learning is used here to recognise patterns in that sensor data that are associated with wear or impending failures, often earlier or more consistently than with fixed threshold values alone.

The result is not a guarantee that a machine will never fail again. It is an estimate of risk, based on data, that helps a maintenance team schedule maintenance at a time that suits the operation, rather than at the moment a machine happens to stop.

What it requires: data, sensors and a baseline

A model that recognises abnormal behaviour first has to know what normal behaviour is. That requires a period of reliable, representative measurement data for each machine or machine component, including situations in which the machine functioned well and, where available, historical failures. Without that baseline and without sufficient failure history, a model is left guessing.

• Sensors that measure relevant quantities (vibration, temperature, current, pressure, sound) with sufficient measurement frequency and reliability
• A period of historical data, including earlier failures or maintenance incidents where possible, from which patterns can be learned
• An unambiguous link between sensor data and the maintenance log, so that measured deviations can be traced back to what actually happened
• Maintenance of the sensors and the measurement chain itself, because a faulty sensor produces misleading signals
• Capacity to recalibrate the model periodically, because machines, loads and conditions change

What it realistically delivers

The honest answer is: fewer surprises, not guarantees. A well-functioning system shifts the balance from reactive maintenance (repairing after something has failed) to planned maintenance based on condition. That makes it easier to fit maintenance windows around production planning, rather than having a breakdown cut across that planning.

Specific percentages for reduced downtime or savings vary so much by company, machine and underlying data that quoting them without your own measurements is not meaningful. A reliable estimate of what predictive maintenance delivers in your situation comes only after a period of collecting and evaluating your own data, not from a general claim.

Fitting into existing quality systems

In food manufacturing, maintenance is not separate from food safety. Maintenance decisions, including those taken partly on the basis of an AI signal, must fit within existing frameworks such as HACCP and FSSC 22000: who decides, on the basis of what information, and how that is recorded. A predictive maintenance signal is an additional source of information for that decision, not a replacement for the existing structure of responsibility or the duty to document.

The human decides and signs

An AI model gives a risk estimate based on the data it has seen. It does not fully know the context of your operation: a large planned order, the availability of an engineer, or a part that was due for replacement anyway. That is why the model advises, and the responsible technical professional decides and signs. In addition, the AI literacy obligation in Article 4 of the EU AI Act has applied since 2 February 2025. Since 27 July 2026, that provision has asked for measures that support the AI literacy of those who work with AI systems, without any specific level having to be guaranteed. In practice, the point is that the technical professional understands how the system arrives at its signals, so that they can work with them responsibly and stand behind the final decision.

How to get started in practice

Do not start with an organisation-wide predictive maintenance programme, but with one or a few critical machines where downtime costs the most or carries the most risk. Establish which sensor data is already available, which is missing, and how complete the historical maintenance log is. Only then can you determine whether a meaningful pilot is possible, and with which model; the free Practice Scan helps you take that inventory.