Predictive maintenance in railways: how sensors and AI are transforming rail operations.

Predictive Maintenance Overview

In railway industry ensuring the safety and reliability of operations is critical. In an era where the industry is going through a digital transformation, predictive maintenance has emerged. Predictive maintenance leverages advanced sensor technologies, machine learning and analytics to monitor health status of rolling stock and infrastructure, ensuring that maintenance activities are performed more efficiently, preventing failures, increasing trains fleet availability and railway network availability for commercial service, and also reducing spare parts costs.

What is Predictive Maintenance

Predictive maintenance uses technology to monitor the health status of railway components. It  uses sensors to collect real-time data. The sensors can monitor various characteristics such as vibration, temperature, as well as as performing a visual inspection of components (such as railway track, train wheels) and consumables (such as train’s brake pads or pantograph carbon strip). After the sensors collect the data, AI-powered data processing systems analyse such data, detect anomalies and flag them to maintenance teams who can then schedule repairs before failures occur.

Sensors in railway predictive maintenance

Vibration sensors: monitor oscillations in components like rolling stock suspension. Abnormal oscillations can indicate early signs of fatigue.

Temperature sensors: monitor heat levels for example in rolling stock braking systems and bearings. Abnormal increases can indicate lubrication issues.

Environmental sensors: monitor temperature and humidity to detect for example conditions that may cause track icing.

Visual Sensors: they use computer vision, relying on cameras and AI algorithms to collect and analyse video data and detect visible defects in a very short time. Computer vision can be used in a variety of inspection applications such as railway track, pantograph carbon strip, wheels, brake pads, joints and bolts misalignments, and doors latching to name some examples.

As a real-world example, Alstom has implemented the Health Hub platform, that uses sensors, machine learning, deep learning and predictive analytics to monitor health status of railway track and moving trains in real-time.

Benefits of Predictive Maintenance

Improved safety: defects can be predicted and detected early, reducing the risk of failures.

Increased trains fleet availability: thanks to a reduction in failures and in the time required to perform quality inspections.

Increased railway network availability: same reasons as above.

Maintenance cost reduction: thanks again to reduction in quality inspection times and also to a reduction in costs associated to spare parts (real-time monitoring allows to change components based on their real condition, allowing a greater lifespan).

Conclusion

Predictive maintenance, thanks to the introduction of sensor technologies like computer vision, and data processing powered by artificial intelligence, combined with Internet of Things and cloud technologies is redefining how railway systems maintenance is performed, resulting in increased safety and reliability of operations.