The power of data: Utilising big data and real-time monitoring to enhance rail operations

How does the use of real-time monitoring and big data positively impact the transport industry, and what benefits can it provide to operators and passengers?

Rail transport is currently seen as the mode of transportation that presents itself as being the best option from an environmental point of view. This means, and we see it already, that demand is increasing and with it a need for higher capacity figures.

Real-time monitoring with the availability and use of data in general can be invaluable in improving the performance of the whole rail system. For example, to avoid disruptions and promote punctuality, which from a passenger’s point-of-view, means a reliable transport service with all the commodities and comfort they need.

Of course, from the operator’s point of view, being able to reduce the costs of rail maintenance and operations is a key benefit of data availability and the valuable insights it can bring when it comes to fleet maintenance and operations. More on this below…

Could you explain the concept of Remote Condition Monitoring (RCM) and how it can be implemented using data analysis?

Rail vehicles for quite some time now have sensors, switches, and complex diagnostic logics for their normal operation. However, most of the data produced for train control and diagnostics isn’t used to its full potential as it isn’t actively used for other applications.  

With the introduction of RCM this data can become remotely available to other relevant stakeholders (operators, maintainers, asset managers, etc.) for their use too. The availability of this data on the shoreside allows these roles to have automated access to information that can be vital in their decision-making process. With this, it means that it is now possible to assess the status of a vehicle, its systems and even create forecasts all from a distance and with the added ability to plan with accuracy when a system needs maintenance or should be pulled out of service.

What are some of the challenges that transport operators face in terms of meeting their Key Performance Indicators (KPIs), and how can data analysis help them to overcome these challenges?

As mentioned, the pressure to improve railway system performance is upon us. Operators are expected to offer more seats, higher frequency and maintain punctuality. Quality of service KPIs such as availability, reliability, safety, and punctuality are key to ensure that service is delivered according to the stakeholders’ expectations and ensuring that rail travel is portrayed as a trustworthy mode of transport.

The insights obtained from data retrieved onboard can support the way operations are managed and can offer insight into how to size availability and how to deal with disruptions.

Being able to understand what is happening in real time allows for better planning, informs decisions, and creates the opportunity to reduce the risk of a train or vital system having a disruptive failure in service which could result in a poor customer perception.

How does RCM work, and what benefits can it offer to operators in terms of maintenance and cost savings?

A RCM project is made up of one or more services.

A project starts with a train data onboard acquisition of one or multiple data sources, train to ground data transmission, shoreside data ingestion, data harmonisation and enrichment, data management and storage, all the way to data visualisation and analytics. On top of this it can also include integration of third-party data and/or opensource data.

The richness of the information produced and the insight that can be harvested from such a system can generate considerable maintenance activity savings, can transform the way it is carried out and can contribute to create a new rail maintenance strategy.

RCM can bring new opportunities to get the most value out of a train system life cycle (associated to Condition Based Maintenance and Predictive Maintenance) by extending its use, allowing more efficient planning of maintenance activities according to the train and system condition and by doing so, reducing unplanned downtime, and improving availability.

Can you discuss the importance of monitoring drivers using data analysis, and how can it help improve safety and compliance in the industry?

The use of remote data acquisition to monitor not only the drivers but all train crew allows a clearer view of how trains are being operated.

Traditionally staff would have to physically visit a train to retrieve the onboard recorder device data locally. This would then be analysed manually to determine if any procedure was breached or if there was a requirement for training or procedure revision. This is a resource and time demanding activity which now, with remote data acquisition can remove the risk of human failure in analysis.

Today with IoT applied to trains this data can be made available automatically and can be analysed without or with minimal human intervention. Any suspected breach can be instantly reported to the responsible stakeholder via SMS or email allowing quicker resolution.  

What are some of the financial and environmental benefits associated with using data analysis to revise components and prolong their life expectancy?

The ability to extend the life expectancy of any system represents a saving. For example, if in a traditional rail maintenance strategy, a component is expected to be revised or replaced n times in the total life of the train. Independent of the condition of that component and its ability to perform, its function will present a cost of n x Y (where Y is the assumed theoretical average cost for each replacement or overall).

Now if we consider the use of Remote Monitoring to track the condition of the component, and only revise or replace it when its condition is going to impact its ability to perform its function, we can assume that in the total life of the train you will only need to do n-a replacements/revisions. Comparing the cost difference between the two scenarios it is clear that: n x Y > (n-a) x Y.

Obviously if a reduction of the number of maintenance events is possible, the amount of material and resources required for these activities will also reduce. On top of this, some of these materials by nature involve a strain and/or risk for the environment in their production and/or disposal phases. Any reduction in their requirement can have a positive impact on the environment.

How does the use of data analysis ultimately improve passenger experience, and what are some examples of how this has been achieved in the transport industry?

The most obvious ones are the ability to deliver a transport service that is safe, punctual, and reliable. Safety is not negotiable and some of the measures in place are probably not as obvious to the passenger, but they are there and supported greatly by data insights.

Data can help identify potential risks and help deliver a higher level of safety to the train system overall.

Punctuality and reliability are obvious performance indicators to a train passenger; if a service systematically struggles to follow a timetable, it affects the passenger’s professional or personal schedule and it might make them decide to use other means of transportation that deliver a more dependable service.

Unreliable train systems could also have a dual impact; direct to the passenger when they struggle to use it as intended, or to the overall train performance with potential impact on service delay or even suppression.

In the first case, the customer feels the direct impact for example: not being able to use a door, enjoying air-conditioning or a being unable to use a toilet that is out of service. Remote monitoring can provide this knowledge on the various onboard train systems allowing operations to mitigate the risk of experiencing such disruptions by making them aware in advance of the issues onboard.