Not so long back, when a piece of railway infrastructure or rolling stock experiences a failure, malfunction, or unexpected breakdown, the railway industry aims to restore the functionality of the equipment or asset as quickly as possible. This reactive maintenance approach is good, but it increases risks, potential delays, or high costs.
Say, when a rail track suffers from a sudden failure, such as a track buckle due to temperature changes or a broken rail, railway maintenance teams would respond promptly to repair or replace the damaged track section to ensure safe operations. However, it leads to delays in trains, frustrates passengers, and expensive repairs. It can be avoided with proactive and preventive maintenance strategies coupled with AI technology.
AI-powered solutions are revolutionizing the railway industry by making it smarter, safer, and more efficient. Rail operators hire AI developers to harness the power of data analytics, automation, and machine learning that enhance the entire rail ecosystem, from operational efficiency and minimizing the occurrence of breakdowns to passenger experience and safety.
Let’s get familiarized with how AI-based predictive analytics and maintenance make railway transportation- future-proof and resilient through the ample benefits it provides and real-life use cases.
5 benefits of using AI-powered predictive maintenance in the railway industry
AI-powered predictive maintenance is transforming the railway industry with its capabilities to predict equipment failures, optimize maintenance schedules, and a lot more benefits. Here are the key benefits that railway industry will reap:
Reduced Downtime and Disruptions:
AI-powered predictive maintenance plays a significant role in reducing downtime and disruptions in the railway industry by identifying potential equipment failures before they occur and enabling timely interventions.
AI solutions continuously monitor the condition of railway assets, such as tracks, trains, signaling systems, and power supplies, and track variables like temperature, vibration, wear, and other relevant metrics. Thereafter, the analysis of real-time and historical data from sensors, equipment, and operational systems helps in identifying patterns and anomalies that indicate impending failures or deteriorating conditions.
The precise failure predictions are auto-generated as alerts or notifications that are directly sent to the maintenance team. It helps the team in gaining insights about the best time to perform maintenance. They can schedule maintenance during off-peak hours, minimizing service disruptions and maximizing uptime.
With Preventive maintenance, rail operators can achieve cost savings by optimizing maintenance practices and reducing operational expenses. With proactive maintenance, the need for emergency repairs, which are often more costly due to unplanned downtime and rush order expenses is avoided.
Scheduling maintenance during planned maintenance windows or off-peak hours reduces the impact on operations and passenger services, leading to cost savings. AI-driven predictive maintenance optimizes the allocation of maintenance resources that prevents overstaffing and reduces wastage, thereby reducing associated expenses.
Predictive maintenance translates into improved safety by identifying potential hazards and preventing equipment failures that could lead to accidents or derailments. The AI algorithms identify early signs of failures in tracks, signaling systems, and rolling stock that allows maintenance teams to address issues before they escalate into safety hazards.
The AI-powered surveillance systems equipped with computer vision can detect obstacles, debris, or trespassers on the tracks that alert rail operators for proactive action and safe operations. The level crossings and gate systems maintenance ensure the proper functioning that further enhances safety for both road and rail users.
Extended Asset Lifespan
AI-powered timely maintenance extends the lifespan of railway assets as AI algorithms predict which component is likely to fail, and thereby premature wear and tear are prevented. Minimized unplanned downtime keeps assets in operational condition, contributing to their longevity.
Prevention of catastrophic breakdowns helps in avoiding extensive damage that could result in irreparable damage to assets. Over-maintenance can accelerate wear and tear, while under-maintenance can lead to early failures. And, AI ensures maintenance is performed when necessary, which enables assets to operate for longer periods.
Predictive maintenance guides lubrication schedules and inspections. Proper lubrication reduces friction and wear, while targeted inspections identify issues early, both contributing to prolonged asset life. Besides, Predictive maintenance helps maintain assets in optimal condition, ensuring that they perform at their best. This leads to improved efficiency, reduced energy consumption, and better overall asset performance.
Data-Driven Decision Making
Real-time data capturing helps in making decisions based on the current asset health. AI-powered predictive maintenance provides actionable insights based on data analysis that helps operators prevent breakdowns and minimize disruptions.
This data-driven decision-making approach ensures that maintenance strategies are well-informed and aligned with actual asset conditions. The condition-based alerts are generated when issues are detected that guide decisions about necessary interventions. Also, AI helps prioritize maintenance tasks by indicating which components are at the highest risk of failure. This information allows decision-makers to allocate resources where they are most needed.
Reduced Spare Parts Inventory
Embracing AI solutions, maintenance activities are more precisely timed. This leads to a reduction in the need for excessive spare parts inventory. Accurate demand forecasting helps in determining the demand for spare parts with higher precision.
The rail operators can order and stock spare parts just in time before they are needed for maintenance which eliminates large stockpiling of parts that may become obsolete or underutilized. Also, AI-based alerts are triggered for the reordering of specific spare parts, ensuring that inventory is replenished only as necessary.
Reduced inventory levels free up capital that can be allocated to other critical projects or investments that contribute to the overall rail infrastructure.
Real-life use cases of predictive maintenance in railway
The real-life use cases highlight how AI-powered predictive maintenance is transforming the railway industry by optimizing maintenance practices, minimizing downtime, improving safety, and enhancing overall operational efficiency in various ways.
Monitor track condition
Sensors placed along the tracks collect data on parameters like track geometry, rail profile, and stress and continuously monitor track conditions. The data analysis helps in identifying deviations from the expected track geometry and rail conditions to detect issues like rail wear, misalignments, and track irregularities.
When deviations or anomalies are detected, the AI system generates alerts for maintenance teams, indicating the specific track sections that require attention. Maintenance teams can prioritize and address issues proactively based on predictive insights.
Identifying anomalies in rolling stock
The vibration, temperature, pressure, speed, and other data are continuously gathered during train operations. AI algorithms analyze the data to establish patterns of normal behavior and variations from these patterns are flagged as anomalies that indicate potential faults. Be it bearing defects, brake system issues, traction motor problems, and more.
AI models use data to forecast which component experience performance degradation. The alert is sent to the maintenance team to provide information about the detected issue and its potential severity. The resources are allocated based on the detected fault’s urgency and the spare parts inventory is managed accordingly.
Component failure prediction
Using AI solutions in the railway industry ensures all the components perform as expected with proactive and preventive maintenance. The components can be brakes, axle bearings, and others that are monitored by AI-powered sensors.
Sensors installed on train axles collect temperature, vibration, and lubrication status-related data. The sensor data is analyzed to establish baseline behavior for healthy axle bearings and identify patterns that correlate with normal operating conditions. The AI system compares real-time sensor data with the established baseline. If there are significant changes in temperature, vibration, or other parameters, it triggers an alert for further investigation.
Using historical data and real-time inputs, the AI model builds a predictive model considering factors like- axle load, speed, and environmental conditions. It predicts when the axle bearings might fail. When the AI system predicts a bearing failure, it sends alerts to the maintenance team. These alerts include details about the specific axle, the predicted time of failure, and recommended actions.
The maintenance team schedules proactive maintenance based on insights that involve inspecting the axle bearings, conducting diagnostics, or replacing the bearings.
Predictive communication system
AI-based communication systems enable rail operators to ensure seamless communication between different stakeholders, including passengers, train operators, and maintenance teams. AI solutions collect data on signal strength, latency, network traffic, and hardware status through sensors and monitoring systems deployed across communication infrastructure such as signal systems, Wi-Fi networks, and intercom systems.
Thereafter signal quality, network stability, and hardware performance are analyzed and compared with standard measurements. When deviations in signal strength, increased latency, or hardware malfunctions are found, alerts are triggered for communication system issues. The warning alerts help in planning maintenance so that network congestion is addressed, faulty hardware is replaced, or network configurations are optimized.
The improved reliability and efficiency of communication systems enhance the passenger experience, operational efficiency, and safety in the railway industry.
Is the industry ready for an AI-powered revolution?
AI-powered predictive maintenance is now actively implemented in the railway industry to enhance operational efficiency, safety, and cost-effectiveness. The real-world usage of AI-driven maintenance by Union Pacific, Russian Railways, Swiss Federal Railways, BNSF railways, and Deutsche Bahn is a clear indication of how AI solutions helped them to prevent power outages, reduce unscheduled downtime, avoid component failure, and increase operational reliability.
It showcases that rail operators across different nations are revolutionizing the way railway transportation is maintained. So, it makes perfect sense to embrace AI solutions and make railway networks and operations more stable and reliable.