Predictive maintenance is one of the great promises of the digital industry. Sensors, data platforms and machine learning are intended to detect failures before they happen, make maintenance more predictable and keep assets available longer. But this is exactly where the misunderstanding begins: Predictive maintenance is not a panacea, but rather a sophisticated tool for clearly defined applications.

Tim Brexendorf, Managing Director of VIDEC Data Engineering GmbH, puts it in a nutshell: “Not every application justifies the effort, and not every company benefits equally.” The decisive factor is not the enthusiasm for new technology, but the question of whether a failure would be really expensive, critical or difficult to control.

High benefit only with high downtime costs

Predictive maintenance is particularly useful where unplanned downtimes rarely occur but then have significant consequences. These include critical production lines, infrastructure that is difficult to access or systems with long restart times. In such environments, simply avoiding fewer failures can justify investment in sensing, data collection, modeling and integration.

The situation is different with easily replaceable components or easy-to-plan maintenance intervals. When spare parts are cheap and downtimes are of little consequence, complex forecasting logic often yields less than providers promise. The benefit does not arise from simply introducing a data-driven solution, but rather from the appropriate industrial context.

Data determines the forecast quality

The technical basis remains a reliable database. Without historical data, sufficient sensor technology and clear contextual information, any prediction remains uncertain. Brexendorf warns accordingly: “Without reliable historical data and sufficient sensor technology, every prediction remains speculative.”

Existing systems in particular often present a difficult picture. Data is fragmented, measured values ​​are inconsistent, interfaces are missing or operating states are not recorded clearly. Environmental conditions, load profiles and usage patterns have a significant influence on the significance. Predictive maintenance is therefore less a pure analysis project than a task of consistent data architecture.

Integration beats model gloss

In practice, it is often not the best model that decides, but rather its integration into the company. A forecast is of little help if it is not automatically incorporated into maintenance processes, production control or corporate resource planning. “Operational value only arises when a warning becomes a concrete work order, spare parts planning or an adjusted production decision,” says Brexendorf.

This is an often underestimated hurdle: Heterogeneous IT landscapes create complex interface problems. Models not only have to be developed, but also permanently operated, monitored and adjusted. If operating conditions change, machine learning models also age. Without regular care, their significance decreases.

Cyber ​​security has been part of it from the start

As machines, sensors and cloud and edge platforms are connected, the attack surface also grows. Manipulated sensor data can trigger incorrect maintenance decisions or specifically disrupt production processes. “Safety concepts must therefore not be flanged on later, but must be part of the architecture,” says Brexendorf. “Anyone who evaluates plant conditions based on data must also ensure that this data remains trustworthy, protected and traceable.”

Use selectively, operate permanently

Predictive maintenance is particularly worthwhile economically when three requirements come together: high downtime costs, a reliable database and a system landscape that can be integrated. If one of these factors is missing, the risk increases that the solution will shine in the pilot project but fail in regular operation.

The hype often obscures the fact that predictive maintenance is not a widespread trend. Used correctly, it can increase efficiency, availability and competitiveness. However, without reliable foundations, it remains exactly what Brexendorf warns about: a costly experiment.