Case Study: Predicting Rail Breaks with AI at ARTC
The Australian Rail Track Corporation (ARTC) manages one of the most extensive and heavily utilised rail networks in the country, where safety and reliability are paramount. Rail breaks present a significant risk, leading to safety hazards, costly repairs, and operational delays. While ARTC had already invested in research with a university to develop advanced predictive models, the challenge was moving from theory to practice deploying those models at scale and running them at the frequency required to be effective in day-to-day operations.
To address this, ARTC partnered with insightfactory.ai to deliver a pilot designed to demonstrate how complex deep learning models could be productionised on the Insight Factory platform. The initiative proved that these models could be run at scale, wrapped with the necessary ML Ops, and aligned to ARTC’s operational cadence, paving the way for predictive AI to play a critical role in the future of rail safety and reliability.
A common challenge organisations face when building predictive maintenance models is twofold: first, creating a reliable model that generates meaningful and actionable predictions, and second, moving that model into production. These models are often built on highly complex feature engineering, drawing on multiple disparate data sources, and while they may show promise in research or pilot form, productionising them at scale is notoriously difficult. Many never make it past the proof-of-concept stage due to the absence of the right infrastructure or the know-how to operationalise them effectively.
At insightfactory.ai, we have invested heavily in solving this gap. Through the Insight Factory platform and our AI Services team, we enable organisations to take massive-scale AI, deep learning, and machine learning models and productionise them in a fully managed way. This case study demonstrates exactly that, helping ARTC move from promising research into a proven pathway for enterprise-scale deployment.
The Problem
As one of Australia’s most critical rail infrastructure operators, the Australian Rail Track Corporation (ARTC) manages an expansive and heavily utilised rail network where safety, reliability, and efficiency are paramount. Rail breaks represented a major operational and safety risk, often resulting in costly repairs, service disruptions, and significant hazards. While ARTC had a thorough inspection regime in place, it was looking to enhance that with predictive foresight.
ARTC had already made progress in this area, partnering with a university to develop research models aimed at more effectively predicting rail breaks. However, the challenge lay in operationalising this work. The models, while promising, needed to be deployed at scale and run at the frequency required to be effective within operations. However, bridging the gap between research and productionisation of the models proved difficult.
What ARTC required was a predictive solution that could take the strong research foundations and implement them at enterprise scale, delivering accurate, explainable, and operationally usable insights to proactively prioritise inspections and mitigate risk.
The Solution
ARTC engaged insightfactory.ai to deliver a demonstrable pilot that proved how research models could be rapidly operationalised on the Insight Factory platform. The objective was to show that models developed in partnership with the university could be deployed at scale, fully integrated with ARTC’s operational systems, and run at the cadence required by the business.
The pilot ingested and integrated diverse datasets including rail cart telemetry, geospatial alignment, rail condition, and weather patterns and applied advanced machine learning models to detect risk patterns and predict likely failure segments. What made the engagement transformative was the ability to wrap the models with full ML Ops capability, ensuring the required inference could be generated at the frequency demanded by ARTC’s operations.
The pilot demonstrated how the models could be embedded into ARTC’s operational context, enabling risk predictions to feed directly into prioritised inspections and maintenance workflows. In doing so, the pilot demonstrated not only the feasibility of deploying these models in production, but also how they could be sustained, scaled, and trusted as part of ARTC’s ongoing safety and reliability program.
The Value Delivered
The rail break prediction pilot successfully demonstrated how complex deep learning ensemble models could be operationalised at scale on the Insight Factory platform. While not yet fully integrated into ARTC’s production environment, the pilot proved that these models could be deployed, run at the cadence required by the business, and wrapped with the necessary ML Ops to deliver reliable, repeatable inference.
The real value delivered was in showing ARTC that large-scale AI and machine learning models can be productionised within their environment, with the Insight Factory providing the scalability, governance, and operational robustness required to meet enterprise needs. This proof-of-capability has opened the door for ARTC to adopt predictive approaches to infrastructure monitoring with confidence, establishing a pathway for AI to become an embedded part of its safety and reliability strategy.
