Predictive maintenance will develop into one of the most pragmatic areas of application for industrial AI by 2026. In contrast to purely data-driven cloud solutions, embedded AI uses local computing power to evaluate data from industrial machines and systems directly where it is generated.

As a result, maintenance decisions become not only more predictable, but also significantly more reliable, because the models can respond to current measurements without transmission delays or dependence on central systems.

Why Traditional Maintenance Strategies are no longer Sufficient

In many companies, maintenance is still carried out at fixed intervals or only when a malfunction occurs. Both approaches have structural disadvantages:

• Interval-based maintenance is often carried out too early and causes unnecessary costs.
• Reactive maintenance occurs too late and leads to unplanned downtime.

At the same time, the volume of data from sensors and machine communication is growing faster than central systems can evaluate it in real time. This is exactly where embedded AI steps in.

Embedded AI as a Core Technology for Predictive Maintenance

By shifting inference to the device level, companies gain immediate access to the current plant status. Local AI models detect patterns, deviations, and trends not retrospectively, but exactly at the moment they occur.

Key benefits include: 

• Direct connection to sensors: Models access raw data without delay, without having to wait for aggregation or transmission.
• Independence from network infrastructure: Analysis continues even with limited or interrupted connectivity.
• Continuous condition assessment: Developments over longer periods of time are detected early, enabling targeted interventions.

Predictive maintenance thus evolves from a downstream analysis process to an active operational tool during ongoing business operations.

Typical Use Cases:

Manufacturing Systems

Vibrations, temperature deviations, or inconsistent power consumption provide early indications of the condition of motors, spindles, or axes. Embedded AI detects deviations that are still below human perception and reports potential malfunctions at an early stage.

Conveyor and Logistics Systems

Load fluctuations, imbalances, or wear signals are continuously evaluated. This helps avoid bottlenecks and allows spare parts to be scheduled based on condition rather than replaced across the board.

Energy and Process Industry

Pumps, valves, or transformers often show early signs of irregularities. Local models link current measurements with historical data and generate reliable statements about the remaining operating condition.

Medical Engineering

Equipment in laboratories or clinics must operate reliably under stable conditions. Local AI enables continuous condition monitoring without sensitive data leaving external systems.

Requirements for Sensor Technology and Data Quality

Predictive maintenance is only as reliable as the source data. The following factors are crucial:

• Precise and continuously recorded measurements
• Correctly calibrated sensors
• Stable time stamping
• Consistent signals without outliers caused by electromagnetic interference or insufficient shielding

Edge-based preprocessing can reduce misinterpretations, for example through filtering, normalization, or feature extraction directly on the device.

Why Local Inference is Crucial

The ability to run AI models directly on embedded hardware is fundamentally changing maintenance processes. Businesses benefit from:

• Significantly shorter response times
• Reduced data load
• direct access to the current plant status
• higher prediction accuracy through immediate interaction with real operating parameters

Cloud platforms remain relevant—especially for model training, long-term analysis, or the central management of multiple systems. However, time-critical decisions are increasingly being made locally at the edge.

Outlook

Predictive maintenance is developing from a purely diagnostic tool into an active control instrument. Interventions are carried out earlier, in a more targeted manner, and at lower cost. At the same time, the resilience of systems is increasing, as malfunctions no longer occur unexpectedly but are systematically detected. Embedded AI significantly shortens the distance between data collection and recommended action—a development that will play a central role in almost every industrial environment in the future.

Learn more about embedded PCs and edge AI platforms from BRESSNER Technology and how they can be used for data-intensive industrial applications.

👉 Learn more about our embedded PCs
👉 Discover edge AI hardware for industrial applications