Bridging the Gap Between the Latest Autonomous and Artificial Intelligence Technology and Railway Maintenance Needs Means Significant Cost Savings for Railways
Autonomous inspection technology, artificial intelligence and big-data analytics offer new paths for inspection without impact on revenue service.
When it comes to successfully identifying derailment risks, railways face two principal challenges: scheduling critical track activities within limited track time without impacting revenue service, and accomplishing that within budget.
Railways are continually tasked with finding creative ways to identify derailment risks and necessary maintenance without taking track time away from revenue cars.
Fortunately, the integration of advanced technology – such as autonomous inspection, artificial intelligence and big-data analytics – offers unprecedented opportunities for planning efficiency and significant maintenance cost savings. By integrating these technologies across the entire continuum of asset monitoring and maintenance planning – from track inspection and data collection to condition trending and data-driven prescriptive maintenance – railway maintenance engineers are realising improved safety and fewer revenue service disruptions.
Data Collection: Autonomous Systems Offer a Highly Efficient Solution
Today, autonomous inspection systems deliver reliable, fully autonomous inspection with systems installed on passenger or freight cars that are in revenue service.
One of the most significant advantages of autonomous inspection technology is that every movement of the host train offers an opportunity to evaluate the track, allowing for more frequent inspections without dedicated inspection vehicles taking up track time.
The use of autonomous inspection technologies can result in earlier detection of track defects, allowing maintenance practices to be preventative rather than reactive. This ultimately reduces the risk of track-related derailments and decreases the cost per inspection.
ATGMS: Earlier Defect Detection
ENSCO Rail, a pioneer in the research, development and delivery of track measurement and inspection technologies, has developed the Autonomous Track Geometry Measurement System (ATGMS). The ATGMS uses the latest fully digital, non-contact measurement technology employed by ENSCO Rail in all of its traditional manned track geometry systems. Measurements are performed every 250mm, up to the maximum speed of the vehicle, and can be performed in either direction. The ATGMS provides real-time transmission of continuous metre- by-metre measurement data, as well as exception processing in accordance with the automatically determined class of track.
More Reliable Detection through Artificial Intelligence
One inherent challenge faced by traditional autonomous track measurement systems is that certain conditions or track features can mimic defects, when in fact track conditions are normal. To remedy this, ENSCO Rail developed advanced artificial intelligence algorithms that recognise and filter out these false positives. The algorithms are based on human data editors from thousands of miles of actual survey data, from which the ENSCO Rail algorithms learned to edit out false positives for real-time reporting.
Big-Data Analytics Offers Condition Trending and Data- Driven Maintenance Planning
Maintenance is a necessary and significant expenditure by railway personnel. Taking a proactive approach to maintenance and asset planning can yield significant savings by reducing manual condition data analysis and unnecessary maintenance expenditures.
New asset condition technology that relies on artificial intelligence, machine learning and data analysis offers the potential for significant reductions in maintenance costs every year while increasing operational capacity through accurate application of maintenance tasks.
AMA: Cost Savings through Predictive Maintenance
The ENSCO Rail Automated Maintenance Advisor (AMA) automatically identifies areas of poor track performance, determines trends in track condition deterioration and translates that data into prescriptive maintenance tasks. The result is proactive and data-driven track maintenance planning and sound, efficient maintenance decisions.
Fully automated and cloud-based, the AMA is flexible and configurable to railway customer deterioration trending needs. It operates automatically, routinely assessing track condition data and recommending maintenance tasks based on a specified maintenance strategy. Asset management plans include rail grinding, rail replacement, ballast renewal, tamping and turnout maintenance requirements.
The railway industry is on the cusp of an exciting new era of innovation in the way technology is applied to safety, operations and efficiency. As next-generation track defect detection and software analysis capabilities evolve, these technologies will optimise railway maintenance and renewal planning, reduce risks through earlier identification of track defects and improve rail network safety.
To learn more about ENSCO Rail autonomous track inspection and data management products, please contact us.
