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Implementing a Predictive Maintenance Strategy for Railways

Last updated on:
August 8, 2023

If you’ve read our thoughts on how predictive maintenance can change the railway industry, you’ll realize that seamlessly integrating them into your daily operations is critical to minimize costly downtimes and increase your overall efficiency. We recently covered what technologies could be used to implement these strategies, and now we will delve into the steps that ApoSys takes with our railway partners to develop and effectively implement a predictive maintenance strategy for them. 

Step 1: Identify maintenance goals and set baseline standards

Identify maintenance goals and set baseline standards for railway maintenance

The first step in developing a predictive maintenance strategy is to define clear objectives and key performance indicators (KPIs). This involves identifying the specific goals that the predictive maintenance system aims to achieve, such as reducing downtime, improving asset reliability, or optimizing maintenance costs. Key performance indicators should be established to measure the effectiveness of the predictive maintenance system and track progress towards the defined objectives.

For rail tracks, it can be especially tedious to monitor its condition and identify the extents of the repairs needed. Even with specialized hi-rail geometry vehicles, there are major inconveniences as roadmasters can only inspect when locomotives are not operating; having limited vehicle assets also slows down the rate of inspection and hinders the overall maintenance cycle. The data received from every locality is regressive, so how accurate could it really be? 

When working with our railway partners, we make sure to form a baseline scan of their rail network, before establishing allowable thresholds and monitoring (in real-time) any changes in the rail condition after each locomotive trip. Our Autonomous Track Measurement Unit (ATMU) integrates a variety of IoT sensors like LiDARs, high-speed cameras, GPS, and Ground Positioning Radar (GPR). Installed at the undercarriage of locomotives, railway operators will receive real-time data on the state of sleeper ties, temperature, vibration, pressure, wear, and the subgrade conditions. This wealth of data forms the bedrock for predictive analysis and informed decision-making. 

Step 2: Data Integration and Centralization

Inspection Data Integration and Centralization

Accumulating data is only valuable when it's effectively processed and analyzed according to the defined KPIs and outcomes. At ApoSys, we work closely with our railway partners to integrate advanced data analytics techniques with the raw data collected, so as to transform it into actionable insights. 

When our railway partners have enough baseline data after the first few runs, we leverage our proprietary machine learning algorithms and big data analytics to process the extensive information generated by the ATMU. These algorithms develop accurate predictions based on historical data, environmental factors, and usage patterns. Anomalies and potential failures are flagged out, which empowers railway operators to optimize their maintenance strategies based on data-driven insights. Over time, the constant monitoring of rail networks will enable our predictive models to evolve and become more accurate. 

To ensure that this data-centric process remains efficient for our railway partners, we employ data reduction techniques to optimize the amount of data processed, so that they are not bogged down with unnecessary storage or processing costs. It also becomes faster for them to act upon the collected data. 

Step 3: Decision-making and optimization

Making data-driven, informed decisions

Based on the insights generated, ApoSys works closely to advise our clients on how they should develop their maintenance workflows and scheduling. This involves defining the maintenance actions to be taken for different scenarios, such as replacing worn-out components, adjusting track parameters, or conducting inspections. Maintenance schedules should be optimized based on the actual condition of assets, ensuring that maintenance activities are performed at the most appropriate time. 

Within the maintenance teams, ApoSys will ensure that they are familiarized with the predictive maintenance software and tools, and trained in interpreting the insights generated by the system. Our in-house developed dashboard will be tailored to engage our users in an intuitive and seamless manner. 

In the long-term, once the predictive maintenance system is implemented, ApoSys will work closely with our partners to monitor its performance and actively improve it. KPIs will be tracked to assess the effectiveness of the system and identify areas for improvement. It is the ApoSys Ambition to align our innovation cycles closely with the evolving needs of our partners. 

Closing thoughts

In the realm of railway maintenance, predictive strategies are the key to unlocking operational excellence. ApoSys leads this revolution, showcasing the profound impact of integrating IoT sensors, big data analytics, and machine learning algorithms. By using our process as a guide, railway operators can embark on a transformative journey toward predictive maintenance excellence. By embracing technology, redefining maintenance practices, and ensuring smoother operations, we are poised to reshape the future of railway maintenance. Connect with us today to embark on this transformative journey together!