Image

Structural Health Monitoring for Predictive Maintenance

Structural health monitoring: from data to insight

Unexpected failures of critical structures and machines are costly, especially when they occur in remote locations or during peak operating periods. In this article, we explain what structural health monitoring (SHM) is, how it enables predictive maintenance, and how NiniX Technologies applies SHM in real-world projects.

What is structural health monitoring?

Structural health monitoring (SHM) is a systematic method for assessing the real-time condition of a structure or component using embedded sensing technologies. By continuously monitoring critical parameters, engineers gain insight into how structures and components behave over time and under real operating conditions.

Once the baseline behaviour has been established, SHM focuses on detecting deviations from these reference values. Early identification of anomalies makes it possible to anticipate degradation, prevent failures and optimise maintenance strategies before issues escalate.

Structural health monitoring at NiniX Technologies

At NiniX Technologies, structural health monitoring is real-world engineering. In close collaboration with our customers, we identify the key parameters and develop tailored, robust monitoring solutions that operate reliably in harsh conditions and deliver long-term performance.

Corrosion monitoring in HVAC installations

In collaboration with Resus, we co-developed the Risycor sensor, an advanced corrosion monitoring device for industrial HVAC installations. The Risycor continuously measures corrosion levels in the piping system, enabling early detection of material degradation. Operators can anticipate costly replacements and schedule maintenance proactively instead of reactively.

Vibration and temperature monitoring in agricultural machines

For large agricultural machines, we developed a customised monitoring system that combines vibration and temperature monitoring in critical rotating components. The system enables early detection of imbalance, misalignment and early-stage wear, which is particularly important for machines operating in remote locations during peak season.

From early detection to predictive maintenance

By capturing and structuring monitoring data, our customers can apply machine learning techniques for predictive maintenance. Instead of facing unexpected breakdowns during peak operating periods, they can intervene early, plan downtime for maintenance and avoid significant repair costs.

The financial impact of preventing a single major failure can be substantial. In some environments, the combined maintenance and operational losses of a breakdown can run into millions of euros.

From measurement to intelligence

SHM only delivers value when raw sensor data is transformed into meaningful insight. At NiniX Technologies, we integrate sensing, embedded electronics, data processing and system architecture into one cohesive solution.

Whether deployed in infrastructure in harsh environments or in heavy-duty machinery, our SHM systems are designed for reliable, scalable and long-term operation. By combining robust hardware with intelligent data interpretation, SHM becomes more than monitoring: it becomes a strategic tool for lifecycle optimisation, improved safety and cost control. Read more about how data is acquired and transmitted in our article on embedded software.

Structural health monitoring for your application?

Exploring how SHM could improve reliability and enable predictive maintenance in your field? Contact our specialists, from concept and development to deployment.

Contact us

Frequently asked questions

Structural health monitoring is a method for assessing the real-time condition of a structure or component using embedded sensors. Deviations from baseline behaviour are detected early, so degradation and failures can be anticipated.

Typical parameters include corrosion, vibration and temperature. Depending on the application, SHM can also address thermal stress and long-term fatigue.

By capturing and structuring sensor data, SHM makes it possible to apply machine learning techniques that predict when maintenance is needed, so downtime can be planned instead of being caused by unexpected breakdowns.

Yes. NiniX designs SHM systems for reliable, long-term operation in harsh environments and heavy-duty machinery.