Predictive Maintenance: How Sensors and Embedded Electronics Help Anticipate Failures

Printed electronics and predictive maintenance: turning sensor data into early insight for smarter industrial maintenance.

In industry, a failure rarely means simply replacing a component. It can lead to production downtime, financial losses, delivery delays, and, in some cases, risks to equipment and operators.

This is where predictive maintenance becomes especially valuable.

Unlike corrective maintenance, which takes place after a failure, or preventive maintenance, which relies on predefined maintenance intervals, predictive maintenance is based on the actual condition of the equipment.

Its objective is simple: detect early signs of degradation before a failure occurs.

Sensors: the first link in the chain

The performance of a predictive maintenance system depends first on its ability to measure the right physical parameters.

 Sensors at the heart of predictive maintenance

Depending on the equipment being monitored, different types of sensors can be used:

• vibration sensors to detect imbalance, bearing defects, or mechanical wear;

• temperature sensors to identify abnormal overheating;

• pressure or force sensors to monitor changes in mechanical stress;

• current and voltage sensors to monitor the electrical behavior of motors or actuators;

• piezoelectric sensors to measure shocks, vibrations, and rapid dynamic changes.

A single measurement only provides a snapshot. Monitoring these parameters over time makes it possible to identify trends, deviations, and abnormal behavior.

Embedded electronics turn measurements into useful information

A sensor alone is not enough.

The measured signal often needs to be amplified, filtered, digitized, and analyzed. This is where embedded electronics play a key role.

A typical acquisition chain can include:

• analog signal conditioning;

• analog-to-digital conversion;

• a microcontroller or embedded processor;

• signal-processing algorithms;

• communication with a PLC, server, or monitoring interface.

The system can then extract relevant indicators such as vibration levels, temperature trends, RMS values, dominant frequencies, threshold exceedances, or abnormal variations.

Detecting degradation before failure

One of the main benefits of predictive maintenance is the ability to act before equipment reaches a critical state.

Take an industrial motor as an example.

A slight increase in vibration may initially appear insignificant. However, when monitored over several days or weeks, it can indicate progressive bearing wear, misalignment, or mechanical imbalance.

Similarly, an unusual increase in current consumption combined with rising temperature may reveal abnormal operating conditions.

By combining data from several sensors, it becomes possible to build a much more accurate picture of the real condition of the machine.

Local processing: a major advantage

Not all data needs to be sent to the cloud.

Local Processing and Edge AI for Predictive Maintenance

With Edge Computing and Edge AI architectures, part of the processing can be performed directly close to the sensor.

This approach offers several benefits:

• reduced data transmission;

• lower latency;

• faster detection of critical events;

• operation even with limited connectivity;

• better control over industrial data.

For example, an embedded system can continuously analyze machine vibrations and only send an alert when abnormal behavior is detected.

Embedded AI models can also go further by identifying complex signatures or anomalies that are difficult to detect using simple thresholds.

Printed electronics open up new possibilities

Predictive maintenance can also benefit from printed and flexible electronics.

Flexible Electronics on PET Substrate with Silver and Carbon Inks

Flexible sensors can be integrated onto curved surfaces, thin structures, films, or areas where conventional rigid electronics are difficult to install.

Depending on the application, printed technologies can be used to measure pressure, force, temperature, deformation, or other physical parameters while remaining lightweight and compact.

This opens up new monitoring possibilities for industrial equipment, smart surfaces, and embedded systems.

A progressive and practical approach

Implementing predictive maintenance does not necessarily mean transforming an entire production line from day one.

A more effective approach is often to start with one critical piece of equipment and one relevant physical parameter.

A typical process can be:

1. identify the phenomenon to monitor;

2. select the appropriate sensor technology;

3. design the acquisition electronics;

4. collect data under different operating conditions;

5. define meaningful indicators;

6. implement thresholds or anomaly-detection algorithms;

7. integrate alerts into the existing monitoring system.

This progressive approach makes it possible to validate the usefulness of the measurements before scaling up the solution.

From sensing to decision-making

Predictive maintenance therefore relies on a complete chain:

From Sensing to Decision: Complete Predictive Maintenance Chain

The quality of the final system depends not only on the sensor itself, but also on the electronics, signal processing, data analysis, and system integration.

At NeoTronis, we work on solutions combining sensors, printed electronics, embedded electronics, data acquisition, visualization interfaces, and local processing to transform physical measurements into actionable information.