Data and AI in Railway Condition-Based Maintenance

Maintaining a railway is hard work. Keeping both rolling stock and tracks in good condition demands increasing amounts of time and money. Naturally, it is imperative for rail networks to find the most cost-efficient and effective ways of maintaining their infrastructure. That’s where condition-based maintenance comes in.

But what is condition-based maintenance? In essence, it involves the continual monitoring of track and rolling stock condition, identifying degradation early, and remedying it before it becomes a serious issue. Digital technology has played, and continues to play, a key role in enhancing the effectiveness of this type of maintenance. Through AI and data analytics, condition-based maintenance has become a cornerstone of modern rail networks—particularly in the context of a rapidly expanding digital railway market, forecast to be valued at $74.8 billion globally by 2024, with a CAGR of 8.4%.

Let’s explore the role of AI and data analytics in condition-based maintenance, the new possibilities each brings to the field, and the challenges network operators face at the intersection of digital and rail technologies.

Big data has numerous applications in modern railways. It is a key component of ‘smart railways,’ encompassing everything from interlocking platforms to traffic management systems. But how does it affect rail maintenance directly?

Fault detection is where big data’s impact is most significant. Data can feed predictive algorithms that anticipate where faults are likely to occur, allowing engineers to resolve issues before they disrupt service. Condition-based maintenance often draws data from multiple sources, which is leveraged to proactively target track sections more prone to failure due to their condition or environmental susceptibility.

However, identifying track faults isn’t as simple as just having data. Techniques like feature extraction and data imputation break raw data into manageable chunks and fill in gaps with accurate estimates. Retrieved data is then compared against expected results from other parts of the track (control limits). A prognostic model predicts the implications of faults, followed by decision-making and presentation of evidence collected throughout the process.

Big data is just one part of the toolbox for condition-based maintenance—but it is an essential one. Artificial Intelligence continues to further reduce dependence on human intervention.

AI in rail maintenance is not new. Its history began in the 1980s with primarily rules-based systems, where humans input simple rules for AI systems to follow. Recent developments have shifted toward machine learning, where AI learns to proactively identify track faults and maintenance issues—and even resolve them.

Predictive maintenance is gaining traction. Sensors placed on tracks can alert engineers to potential faults, with warnings communicated between trackside sensors and onboard systems. AI-based maintenance extends beyond track monitoring: SNCF, France’s national rail operator, has used AI for everything from onboard air conditioning units to lineside pantographs. This enables real-time fault detection and resolution while reducing costs. Efficiency gains of 10–15% are estimated globally through real-time condition-based maintenance, freeing funds for other infrastructure investments.

AI and big data in the rail sector are still in early stages. Initiatives like the European Union’s Horizon 2020 program provide funding for technology adoption on rail networks and support startups driving innovation. AI is central to the EU-funded OPTIRAIL scheme, which advances fault detection and prevention and contributes to a long-term understanding of network conditions.

In the UK, plain line pattern recognition leverages AI to identify track faults remotely. Cameras along the lineside capture images at different times, and AI algorithms highlight physical changes and alert engineers remotely. LiDAR technology also helps prevent geotechnical faults by generating Digital Terrain Models. Over time, machine learning and cloud-based analysis detect geographical threats to track sections before they become dangerous.