Avoiding Costly Train Derailments Using Digital Twins
How the digital twins, AI/ machine learning, and real-time monitoring can ensure rail safety.
Advanced digital twins are poised to offer analytics managers an innovative solution to detect and prevent potential issues with precision. Dr. William Bain, CEO of ScaleOut Software, says by harnessing the synergy of AI, machine learning, and in-memory computing techniques, digital twins can address paramount safety issues such as train derailments.
Recent advancements in real-time computing have transformed digital twins into a vital technology for tracking complex systems and improving decision-making. These new use cases enable capturing and analyzing data more effectively, providing needed insights to make critical decisions before opportunities are lost. This newfound capability can empower leaders to take proactive measures and avoid potential crises, representing a significant advance in their ability to manage and respond to complex situations in real-time.
This shift in the use of digital twins expands beyond earlier applications in product lifecycle management, which helps evaluate how a single product, such as a jet engine or wind turbine, would perform before deployment and later in real-world situations. New technology enables digital twins to monitor large, dynamic data systems like nationwide rail networks, vastly enhancing their real-time monitoring capabilities. Digital twins also excel in predictive modeling, simulating the behavior of thousands of interacting entities to prevent issues. While digital twins can provide valuable insights to decision-makers in a wide range of use cases, this article focuses on a key concern for public safety: train derailments.
Using Digital Twins to Safeguard Against Rising Train Derailments
Despite enhancements to accident prevention systems deployed in the rail system, their effectiveness has substantial room for improvement. In 2022 alone, 1,164 derailments wreaked havoc in the U.S., inflicting over $220 million in damages. High operating temperatures of essential components, such as wheel bearings, often emerge as culprits. To monitor wheel-bearing temperatures, approximately 6,000 hot boxes along railways across the U.S. report temperature data as trains pass. However, the wealth of data gathered by these hot boxes must be utilized to predict imminent bearing failures. The potential for improved safety lies largely untapped as data from detectors remains disconnected and fails to undergo real-time correlation to detect trends. This missed opportunity can have dire consequences.
Now, consider the latest breakthroughs in digital twin technology, which offer a new opportunity to address this challenge. These advanced digital twins empower rail operators to identify wheel-bearing issues and proactively prevent derailments. They can monitor the entire nationwide fleet of active rail cars in real-time, pinpointing potential problems in milliseconds. Unlike current safety systems, they have the power to track temperature fluctuations in wheel bearings as railcars travel from one detector to another, enabling them to discern temperature trends and forecast impending failures well in advance.
See More: Digital Twins Enhance Decisions But Only With the Right Context
Digital Twins and Artificial Intelligence
Digital twins can harness AI and machine learning principles to help identify patterns and anomalies, increasing their predictive capabilities. For example, the analytics code running in each digital twin can use machine learning algorithms to spot irregular temperature trends, unexpected variations across axles and wheels, and other anomalies. Machine learning algorithms can draw on thousands of measurements and other data from rail lines for supervised training sets to establish a typical safe temperature range for wheel bearings. Machine learning can help establish baseline statistics and quickly identify deviations that demand immediate attention by seamlessly integrating data from hot boxes and storing it within each digital twin.
At the same time, digital twins can maintain a precise record of each rail car’s service history, consider contextual data like weather conditions, and consider a range of other factors, further strengthening their analysis and decision-making capabilities. When these technologies work in tandem, they can trigger real-time alerts to train engineers, halt potential derailments, and even foresee impending events with remarkable accuracy.
Digital Twins and In-memory Computing
In-memory computing technology provides a critical breakthrough that powers digital twins for real-time monitoring and predictive modeling at scale. It deploys digital twins on a scalable cluster of servers in the cloud or on an on-premises infrastructure. By storing data in memory and harnessing the combined computing power of multiple servers, this technology enables thousands of digital twins to rapidly access their contextual data and analyze incoming information in milliseconds. In-memory computing enables digital twins to take immediate action in real-time to prevent potential disasters like train derailments. In contrast, traditional offline analytics platforms often rely on slow database retrieval, resulting in bottlenecks that hinder real-time responsiveness.
Because it can efficiently run huge populations of digital twins, in-memory computing also enables digital twins to simulate large systems with complex interactions for predictive modeling. This helps system managers evaluate the effects of dynamic behaviors, such as weather events, to make better decisions and ensure smooth operations. It also lets them test real-time monitoring systems in various scenarios before deployment. For example, they can measure machine learning algorithms for detecting wheel bearing failures in a simulation that models the U.S. railway network to validate their effectiveness.
Harnessing the Power of Digital Twins and Cutting-Edge Technologies
Train derailments pose a pressing and financially burdensome challenge, and new digital twin technology offers a breakthrough in addressing this issue. The use of digital twins in railway operations has the potential to deliver continuous real-time monitoring and provide much deeper introspection on data that’s already available today. This technology holds immense promise to prevent future train derailments, marking the first step in enhancing railway safety.
The result is even more powerful when digital twins join with other cutting-edge technologies, such as artificial intelligence and in-memory computing. Machine learning methods enable analytics code running in digital twins to discern irregular trends, and in-memory computing technology provides the computing power needed to provide real-time monitoring and predictive modeling at scale. These combined technologies empower rail operators to detect potential issues with exceptional precision and proactively implement a new generation of preventative measures that safeguard rail systems and passengers.
Looking ahead, digital twins are poised to expand their role to monitor other critical components in the railway system, such as switches and crossings. This evolution promises to foster the modernization of railway infrastructure, improve public safety, reduce costs, and enhance the efficiency of rail operations. As this transformation unfolds, digital twins are ready to offer a key role in building solutions to the rail industry’s complex safety challenges.
How can advanced digital twins powered by AI and real-time monitoring transform rail safety? Let us know on Facebook, X, and LinkedIn. We’d love to hear from you!
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