Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many Original Equipment Manufacturers (OEMs) are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: big data.

In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). Thanks to the rapidly expanding scale of manufacturing and asset maintenance industries, they are now adapting to the wider applications of advanced algorithms on consumer generated big data.

Though CBM and PM are commonly adopted practices in rail industry, the scope of CBM is far wider than that of PM. CBM usually constitutes the direct application of diagnostic monitoring in real-time, making sense for assets and sub-systems that give us enough time to act after reading their condition.

It also makes perfect sense for most of the diagnostic monitoring scenarios such as monitoring wheels, axles, high level network issues etc. However, CBM fails to address those systems which don’t give enough time to act up on or systems that directly influence customer experience.

For example, if you identify a malfunction in the doors using a diagnostic monitoring system, there is rarely time to fix them without impacting delay and customer comfort. Ideally, OEMs want to be aware of any potential failures well before they happen, and act upon them so that they don’t happen during production/operation.

A PM solution helps to manage these assets so that OEMs will not end up in such a ‘failure’ scenario. Similarly, with mission-critical safety-related systems like bridges, some of the signalling assets that simply cannot be allowed to fail are good candidates for the implementation of PM solutions. For all other assets, CBM is sufficient and PM may not be required, because there is enough time to take action.

However, the application of big data is not limited to CBM and PM. As the industry produces more data from its assets and from a variety of maintenance management systems, proven big data applications from other industries (such as in e-commerce, social media, online search etc.) become increasingly relevant to the rail industry. One of these applications is in Artificial Intelligence (AI), which is already being applied within rail in a variety of ways:

Situational intelligence

Usually, Situational Intelligence means having complete knowledge of the operations and greater control over bringing things into order, if required. Train operations companies (TOCs) can achieve situational intelligence by collecting real-time data from their trains as they are in operation and analysing it in three different dimensions: Spatial, Temporal and Nodal.

Firstly, the spatial component gives the real-time location of trains and their respective systems and sub-systems in each train. With this, you can understand where things are and how they are performing through geospatial orientation.

The temporal view can then be added to provide insight into the performance of assets according to a time scale – spanning from how they are performing in the real-time to the last minute, hour, day, and week and beyond.

The final element of situational intelligence is nodal, which is the inter-relationship and hierarchy of various sub-systems across the entire fleet. Analysing data along nodal dimensions gives us the ability to view the interdependencies of various systems, and exposes root causes for failures and system behaviours.

Analysing big data in these three dimensions in real-time works like a ‘collective mind’ that can expose both opportunities and threats in train operations. Its combination with recent advancements in computational efficiency now allows for massive scale searches for anomalous patterns, which produces results in minutes. As a result, comprehensive surveillance becomes possible and critical clues for impending failures become much harder to miss.

Some of the advanced statistical analysis models available today can produce ranked lists of findings, so that investigative resources can focus on those which are the most significant and alarming.

Once the most frequent and common issues are identified, routine analysis can be automated and run regularly, leading to more effective resource allocation. Certain industries like utilities are already using such systems today for network monitoring and asset maintenance.

Operational intelligence

Operational intelligence meanwhile uses the power of data and its capability to extract the right information at the right time to improve the effectiveness of rolling stock maintenance.

For an example of how this might work, think of the product recommendation engine on Amazon.com. Or friend suggestions on Facebook or contextual ads on Google search: welcome to the world of Artificial Intelligence.

With the growing volume of sensor and maintenance data beginning to reach the scale of consumer-generated data on the internet, the rail industry is now ready to exploit the capabilities of AI.

Challenges such as identifying a root cause to a problem and finding the most suitable repair action are very similar to those already solved in the consumer space, where AI is already widely used. Today cutting-edge algorithms can mine tons of operational data for TOCs and provide them with recommended actions for most unscheduled maintenance issues.

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