Predictive maintenance – the ability to fix failures before they happen, may sound like a futuristic concept but it is, in fact, implemented in various ways by the railway industry across the globe. As the technology develops, the possibilities become endless and its potential positive impact on performance, customer experience and costs is undeniable.
Below we’ll look deeper into the subject to explore the application of predictive maintenance in the railway industry, as well as the potential risks and vulnerabilities it’s exposed to and how advanced Cybersecurity can prevent those.
What is predictive maintenance?
Simply put, predictive maintenance is a system that collects and analyses data to make predictions as to when a certain part of the train or the railway is likely to fail in order to get it repaired before the failure happens.
Traditionally, maintenance is done at regular intervals to prevent technical problems caused by wear and tear. The problem is that unexpected issues and premature deterioration caused by various factors can affect parts of the railway system before the scheduled routine maintenance takes place.
Predictive maintenance offers an intelligent solution that allows for developing issues to be flagged in real time, triggering the maintenance and servicing process as a preventive measure. It takes a proactive approach to maintenance with the goal to eliminate technical problems before they have caused major disruptions or accidents.
Top 5 Benefits of Predictive Maintenance for the Railway
Predictive maintenance technology could have multiple applications in the context of the railway and it has the potential to improve health and safety, as well as to save time and money. Let’s look at some of the main benefits of implementing predictive maintenance tools below:
Reduce downtime
Nothing is as disruptive as long delays on the railway. Predictive maintenance technology can help keep the trains running on time by detecting issues before they affect the railway system, whether those are related to the fixed, linear or mobile assets. This approach includes identifying signs of deterioration or below-optimal performance and proactively investigating the cause before the developing issue takes a train or an entire portion of the railway out of service.
Prevent damages to the train and the rail
Data captured and analysed using predictive maintenance technology can be a powerful tool when it comes to reducing damages both to the machines and the railroad itself. For example, a 3-axis translational accelerometer was mounted on the steering wheel to measure vibrations caused by wheel flats during acceleration. Being able to identify unusual vibration frequencies was shown to allow for better maintenance planning for the train wheels.
Improve void detection
Void sleepers are one of the common safety concerns on the rail track. They can be the cause of various issues, ranging from a bumpy ride and mild inconvenience for the passengers to major rail breaks and train accidents. Using a monitoring system attached to the driving trailer, void detection can be improved and the level of risk posed can be assessed based on the data, which would, in turn, inform predictive maintenance measures.
Limit point machines failure
Point machines are a key element of the railway system. They are used to operate the rail turnouts that guide the train from one track to the next. With time and use, point machines gradually start degrading creating a preposition for serious railroad accidents, such as derailment. Predictive maintenance can be used to detect the warning signs of the devices becoming worn out to a breaking point before their regular maintenance is due.
Optimise train servicing & repairs
When a failure, such as a train door malfunction, is reported, the engineers often have nothing to go by other than a report given by the driver, who on his part, has received information from passengers on the train manager. As a result, if the issue cannot be replicated, the cause may not be identified and the affected carriage can be put back into use without having been fully repaired, leading to another breakdown soon after. By collecting and analysing data on the movement and performance of the train door, the issue can be eliminated before it affects the passengers by using reduced closing speed as an indicator for a possible upcoming failure.
Predictive Maintenance and Cyber Security
When it comes to predictive maintenance, engineering innovation goes hand in hand with IT. From prompting the need for earlier maintenance to implementing automated safety measures, such as system shutdowns triggered by a series of warning factors, computer software is used to leverage the data collected and analyse it to produce actionable data.
In addition to this, predictive maintenance solutions, as well as other digital technologies used to optimise the efficiency of the railway system, such as energy optimisation solutions and monitoring systems used to track trains along the track, all connect to the Internet. This makes them a potential target for data breaches, ransomware attacks and other malicious activities by hackers.
For example, the Massachusetts Bay Transportation Authority (MBTA) has been a victim of multiple attacks, from hackers taking control of a moving train in 2015 to a ransomware attack in 2020. With predictive maintenance becoming a significant part of Transport for London’s (TfL) asset management strategy for the London tube, these types of incidents are well worth looking into from a UK perspective.
Working with enterprise-level Cyber security experts offers the best defence against remote attacks that can disrupt or even completely disable railway operations nationwide. It also ensures that Cyber incidents are responded to quickly and efficiently to minimise any damages caused by a successful breach. This is why it is particularly important to invest in an advanced multi-layer Cyber security solution to protect the railway and its assets.
