Predictive maintenance in automation – how to use encoders, transducers, and position sensors

Predictive maintenance is an approach that makes it possible to detect signs of wear and irregularities before they lead to machine failure. In practice, it’s not just about “collecting data,” but about interpreting it correctly—identifying changes in position, speed, motion repeatability, or axis stability before they result in production downtime. This is why encoders, position transducers, and position sensors play such a crucial role in predictive maintenance.

In industrial plants where continuity and service predictability are essential, properly selected measurement components enable a shift from a reactive model—“fix it when it breaks”—to one based on analyzing symptoms and planning actions in advance. This translates not only into fewer failures but also better control over maintenance costs.

Why predictive maintenance changes the way we think about automation?

The biggest advantage of predictive maintenance is the ability to act before a machine stops. Instead of scheduling inspections purely based on time or operating hours, maintenance teams can make decisions based on the actual condition of components. As a result, it becomes easier to reduce unplanned downtime, make better use of service resources, and extend the lifespan of critical system elements.

In practice, predictive maintenance works particularly well in applications involving linear or rotary motion, high cycle repeatability, and processes sensitive to deviations. In such cases, even a small change in the signal from an encoder or transducer can be the first warning sign of mechanical wear, increasing backlash, or declining accuracy.

What problems can be detected early?

Predictive maintenance helps identify, among others:

• deteriorating position repeatability,
• increasing backlash in the mechanical system,
• axis speed instability,
• incomplete extension or retraction of linear elements,
• deviations in geometry and tilt,
• rising motion resistance or overloads.

This is where measurement components play a key role. Without reliable data on the actual behavior of axes and drives, predictive maintenance remains just a buzzword.

The role of encoders in predictive maintenance

Encoders are the primary source of information about rotary and linear motion. They allow monitoring of position, speed, and direction, and—when properly analyzed—can also reveal symptoms of system degradation. If an axis begins reaching positions with greater deviation than before, shows minor speed fluctuations, or requires more frequent corrections by the drive, this may indicate a growing mechanical or electrical issue.

In predictive maintenance, encoders are not used solely for control—they also provide diagnostic data that helps detect when a system is no longer operating as stably as it once did. This applies to both incremental and absolute encoders, with the latter offering an additional advantage in applications where continuous position information after power loss is critical.

Which encoder signals are most useful?

The most diagnostically valuable signals include:

• deviations from the target position,
• changes in acceleration and deceleration dynamics,
• unexpected drops or fluctuations in speed,
• increased number of corrections in closed-loop control,
• loss of synchronization between axes.

When encoder data is analyzed over time, it becomes possible to identify trends that are not yet failures but clearly indicate the system is moving outside its “normal” operating profile.

Position transducers – monitoring linear motion

In many machines, what matters most is not the motor rotation itself, but the actual position of the working element. In such cases, position transducers—especially draw-wire (string) transducers—are highly effective, enabling continuous measurement of displacement in linear applications.

In predictive maintenance, position transducers help detect:

• increased time to reach a position,
• instability during movement,
• incomplete closing or return of an element,
• repeatable deviations at end positions.

This is particularly useful in systems with actuators, guides, lifts, and telescopic mechanisms, where wear or increased friction may not cause immediate failure but already affects motion parameters.

Position sensors and inclinometers – an additional diagnostic layer

Predictive maintenance is not only about drives but also about the overall geometry of the machine. This is why position sensors and inclinometers are important—they allow monitoring of tilt, deflection, and changes in the positioning of working elements.

This is especially relevant in:

• mobile machines,
• special-purpose vehicles,
• long-arm systems,
• structures exposed to vibrations and positional changes.

If an inclinometer shows that a system is operating at a slightly different angle than before, it may indicate wear in support points, improper loading, or an issue with axis guidance.

How to implement predictive maintenance step by step?

Effective implementation of predictive maintenance does not start with buying random sensors, but with answering three key questions:

1. Which machines are critical to production?
2. Which parameters should be monitored?
3. How will we recognize that the system is starting to operate incorrectly?

Only then should appropriate measurement components be selected. The next step is integrating them with PLC, SCADA, MES, or another data acquisition system. It is equally important to define alarm thresholds and signal interpretation rules. Data alone is not enough—you also need to know when a trend is normal and when it should trigger maintenance action.

What does a well-designed predictive maintenance system provide?

A properly implemented system based on encoders, transducers, and position sensors delivers three key benefits:

• fewer unplanned downtimes,
• better service predictability,
• greater process stability.

Additionally, it supports decision-making. Instead of relying on intuition, maintenance teams have access to concrete data showing which axes, drives, or mechanisms are beginning to deviate from normal operation.

Predictive maintenance in automation does not rely solely on advanced software. Its foundation is reliable data from properly selected measurement components. Encoders track position and speed, position transducers reflect actual linear movement, and position sensors and inclinometers help assess the stability and geometry of the entire system.

If you want to implement predictive maintenance effectively, start by analyzing critical machines and selecting the right measurement components. The quality of this data determines whether your system will merely “collect signals” or genuinely help you predict failures and reduce maintenance costs.