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Predictive maintenance in railways: Exploring best practices with IoT


In the UK, only 38% of rail passengers are satisfied with the way rail companies deal with delays that are often caused by asset failures. It speaks eloquently to the fact that while maintenance is the bulk of costs for the rail industry, the current approaches can’t provide an adequate service level. With the advent of the internet of things (IoT), predictive maintenance in railways has proven to be the most effective and promising maintenance strategy. However, most of the railway assets in the world are still not equipped even for real-time monitoring. How critical is this situation?, says Julia Seredovich is business operations manager at Professional Software Associates Inc.

The need for Predictive Maintenance (PdM) is felt differently across different regions and depends on the traffic of a particular railway network. In the US, where there has been a significant increase in demand for rail transportation over the past five years, the infrastructure must be in perfect condition to withstand such an increase in loads. In such cases, you can hardly avoid Predictive Maintenance if you want to minimise unexpected failures, and preferably eliminate them. Can we achieve this? To find out, PSA shares its expertise on PdM use cases and IoT technologies that have shown themselves to be the most promising PdM solutions for rail, focusing on how to approach them reasonably. 

Applicability of predictive maintenance in railways

  • Predictive Maintenance – is an approach that allows service activities to be carried out only for vulnerable components of equipment or structures before the probability of their failure reaches a maintainable limit. This method is based on the processing of actual, real-time data of specific equipment, as opposed to scheduled maintenance based on statistics. The principle of PdM is simple by monitoring changes in the machine’s parameters in real-time, we can calculate its remaining useful life (RUL), and schedule maintenance accordingly. 

The data that allows the execution of PdM can be fully provided automatically through IoT, but to make it cost-effective, the enterprise needs custom research & development activities. Technically, solutions for Predictive Maintenance in Railways should contain the following components: 

  • Sensor-based and external data collection. By equipping rail assets with IoT sensors, their continuous and remote monitoring becomes possible. The higher the volume of data we gather, the more chances we gain to build a robust analytical model capable of the most accurate predictions. Also, the precision increases to the extent that we consider more factors by including various data from different sources. Historical data from field inspections, engineering data on the mechanical parameters, remote control systems data, asset management data, and external data like weather conditions or GPS coordinates might be relevant for building a workable predictive model. 
  • Data transmission to the cloud. Reliable connectivity should be established to transmit the data generated in the field in a real-time or close to real-time manner. This can require the installation of additional equipment, like a radio tower, implementation of cellular-based data transmission, and wired or wireless communication – these are points to pay attention to. Generally, MQTT, XMPP, AMQP, or WebSocket are applied for PdM using machine learning, while train-to-ground applications can be built using LTE or WLAN. 
  • AI-based data analysis. By picking up the relevant status indicators that notify upcoming failure, you can build a robust predictive-analytical model, and then deploy it in the cloud. Thus, the real-time field data can be processed and analysed in a cloud application to give accurate predictions on RUL, as well as create reports. 

Although Predictive Maintenance has only started to gain momentum in rail, positive results are already seen. Also, the world’s best global practices on PdM allow the operator to get the most out of its implementation reducing the number of trials and errors, and, therefore, iterations.

Predictive maintenance in railways: Use cases

Predictive Maintenance in Railways covers digital and mechanical devices, as well as engineering structures. These components can be considered mission-critical and safety-critical, which makes them target assets for a Predictive Maintenance implementation. The approach slightly differs for every case, but the principle is the same. For digital devices, we measure their electronic health, for mechanical, functional health, and for construction, structural integrity. Predictive Maintenance is recommended for railways also since it involves non-destructive inspections, without affecting the structure or its components. 

Predictive maintenance for level crossings

Level crossing failures take first place among the causes of rail accidents in Europe. Due to a lack of digitalisation and control, a crossing breakdown can become known only after public reports. Even real-time monitoring can significantly reduce incident-response time, but PdM enables more advanced opportunities. For example, by equipping crossings with hardware that senses the gate opening angle, it’s possible to track its decrease over time and link this to upcoming failure. Since level crossings deteriorate gradually, the algorithm makes an assumption about remaining useful life. Maintenance activities can be planned automatically.

RUL for the gate control device can also be calculated by measuring current surges, unstable voltage, high temperatures of the hardware, and the oxidation of its components. PdM solutions can track how the performance of the equipment decreases to make an accurate assumption on when it might fail.

Predictive maintenance for bridges

Violation of the structural integrity of railway bridges is the most dangerous, costly, and long-term failure to fix. Unfortunately, issues related to the structural health of rail bridges are inevitable, since the actual loads often exceed the design ones, while the operating conditions are frequently tough. IoT-based analytical tools can combine the info regarding static and dynamic loads, and the quality of materials with the actual state of the bridge to make accurate predictions when it might fail. Once the bridge reactions on various loads have been simulated, the model can be supplemented with additional parameters, such as bridge behaviour during seismic events, rail profile irregularities, and so on.

Major cracks that might cause bridge collapse do not appear at once. They result from minor cracks that have violated the structural health of the bridge for weeks or months. In this regard, vibration diagnostics, as well as continuous visual monitoring of displacement, deviation, and inclination, have proven themselves well. 

Predictive maintenance for tunnels

Rail tunnels can suffer from seepage, delamination, cracks, delamination of concrete, corrosion of steel, drainage, and structural failure of the tunnel lining. In particular, water leaks in the concrete lining of the tunnel usually cause corrosion of the steel reinforcement. To prevent such cases, IoT sensors can provide visual and sonic inspections to identify stratification of the concrete; ultrasonic sensors and radars measure speed in the material to monitor its structural integrity; magnetic and electrical inspections immediately identify corrosion. By adding info on maintenance and repair history, external loads, mechanism of failure, and so on, you gain a complete view of tunnel behaviour over time, allowing for optimising maintenance strategies.

Predictive maintenance for signalling infrastructure

When it comes to track circuits, railroad switches, and axle counters, we first speak about failures that are caused by external factors, such as weather conditions. Thus, their real-time monitoring comes down to the protection against contamination that might lead to icing, corrosion, etc. Indeed, here we speak more about condition-based than predictive maintenance since weather conditions can hardly be calculated in advance. By detecting any kind of debris as soon as it appears, an IoT-based management platform can instantly provide the alarm on it informing dispatchers and maintenance teams on issues.

To have a complete view of maintenance needs for railway signaling solutions, it’s valuable to equip it with additional sensors. Thus, all the critical data, such as voltages in track circuits, current power consumption, time of the points shifting, and cable resistance, will promptly reach the control centre, enabling reliable monitoring. The keyword here is “promptly” since it eliminates delays-related costs and significantly reduces incident-response time if the failure still happens. 

When it comes to outdated control devices, IoT has a workable practice of how to get legacy systems online utilising edge devices containing both outdated and modern communicating protocols.

Predictive maintenance in railways: Summing up

Julia Seredovich
  • Predictive Maintenance has only started to gain momentum for rail applications but has already proven its value for different rail components. 
  • To implement PdM for rail assets, it’s crucial to provide the data as fully as possible, with engineering, maintenance, external, and control data at least; consider harsh conditions of exploitation to ensure stable connectivity; take time to build a workable analytical model for every type of asset. 
  • By applying Predictive Maintenance for engineering structures, you gain continuous monitoring of their structural health, which can be established via visual, vibration, sonic, magnetic, and other inspections. This allows for tracking minor cracks, inclinations, or deflections before they lead to major issues. 
  • PdM for control devices allows for tracking their performance changes that might lead to failure.

The author is Julia Seredovich is business operations manager at Professional Software Associates Inc.

About the author

Julia is Business Operations Manager at Professional Software Associates Inc. (PSA). Thriving at the junction of railway signaling and the industrial internet of things inside PSA, Julia specialises in building client solutions that bring together signaling technologies and overall business objectives. A liaison to PSA’s invaluable technical engineering team, she provides companies with expertise in the signal design of electrical interlocking, microprocessors and relay-processor systems.”

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