Pecovasa Renfe Mercancías
IoT-Driven Rail Freight – More Efficiency, Safety, and Transparency
Industry: Logistics & Transportation
As one of the leading providers of rail freight transport on the Iberian Peninsula, Pecovasa moves thousands of tons of cargo safely and reliably every year.
Rising demands for operational safety, sustainability, and efficiency pushed existing maintenance models and operational processes to their limits. To reduce downtime, extend the lifespan of critical components, and increase operational transparency, Pecovasa transitioned to fully digitalized technical maintenance – with a focus on IoT data and predictive analytics.
Status quo
Previous processes were mainly based on interval-based maintenance and expert judgment. Data on mileage, load, driving behavior, or environmental conditions was either not collected or only recorded in isolation.
Technical components such as running gear, axles, or braking systems were maintained independently of each other – without considering their actual wear levels.
At the same time, there was no real-time visibility into load levels, driving profiles, or operational deviations. A major issue was the lack of systematic analysis of sensor data, despite trains, wagons, and components generating vast amounts of operational data. This led to inefficient maintenance cycles, unnecessary downtime, and poor maintenance planning – especially during high network utilization.
The challenge
The challenge was to turn vast amounts of unstructured sensor data into actionable operational insights.
The goal was to correlate factors like vibration, temperature, humidity, acceleration, braking behavior, and GPS position in a way that enables precise assessments of the condition of individual components. At the same time, operational data such as JERK energy, vibration amplitudes, and GPS-based driving profiles needed to be analyzed intelligently to detect critical load peaks.
It was crucial to identify patterns indicating potential failures or wear — regardless of whether they were caused by track conditions, weather, load weight, or dynamic driving maneuvers. The analysis also had to be performant enough to operate in real time and integrate seamlessly into existing maintenance processes without delays.
The solution
The solution is based on real-time collection and analysis of extensive sensor data from trains, wagons, and components.
The platform enables seamless live tracking of all transports and supports intelligent route planning that dynamically adapts to operational conditions. In addition, it was fully integrated with existing planning and billing systems.
The infrastructure remains fully scalable at all times, operates reliably even under high load, and meets the highest security standards. This creates a foundation for designing more efficient processes while making rail freight operations more flexible and reliable.
The result
Pecovasa now benefits from real-time tracking, improved traceability, and precise analytics.
The solution enables lower maintenance costs, reduced downtime, and significantly higher operational reliability. This project sets new standards for sustainable digitalization and modern transport management.
Technical
Highlights
Even complex projects can be summarised in clear technical terms. Key figures and distinctive features provide insight into the concrete implementation – measurable, tangible, and transparent.
- Real-time geolocation and live tracking
- Analysis of comprehensive sensor data from trains, wagons, and components
- Data filtering with Moving Average, Kalman, and Wavelet filters
- Pattern recognition using Dynamic Time Warping (DTW)
- Detection of load patterns, vibration levels, JERK energy, and equipment status
IoT-driven digitalization has taken maintenance and operations at Pecovasa to the next level. Real-time data, precise pattern detection, and intelligent analytics ensure maximum operational reliability, reduced downtime, and fully optimized maintenance – efficient, scalable, and future-proof.
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