SMRT taps AI and analytics to predict rail faults and speed up maintenance

Keeping trains humming along safely and smoothly across Singapore’s rail network is a monumental task, especially when engineers have only a three-hour window each night to fix track faults. Now, rail operator SMRT has a new artificial intelligence (AI)-powered tool to help: Jarvis.

Playfully dubbed “Just Another Really Intelligent System” by SMRT staff, the new intelligent analytics platform was developed by Strides Technologies – SMRT’s engineering and tech innovation arm – together with tech giant Oracle.

Announced at the Oracle AI World Tour Singapore on 14 April 2026, the platform leverages Oracle Cloud Infrastructure (OCI) Enterprise AI and the Oracle Autonomous AI Database to consolidate over 30 years of operational, engineering, and failure pattern data.

This vast trove of data, previously distributed across multiple systems in the form of text, graphs, and flowcharts, is now accessible to maintenance teams through a generative AI chatbot interface powered by large language models and vector search to help them make better-informed decisions.

The result is a system capable of supporting predictive maintenance using machine learning algorithms, enabling faster fault resolution and contributing to SMRT’s mean kilometres between failure (MKBF) metric, an industry-standard used to measure rail service reliability. In Singapore, the Land Transport Authority sets a strict MKBF target of one million train-kilometres, a benchmark that public transport operators must consistently meet to ensure minimal commuter disruption.

During a discussion with Chin Ying Loong, Oracle’s senior vice-president and regional managing director for ASEAN and South Asia growing economies, SMRT group CEO Ngien Hoon Ping said one of Jarvis’s biggest benefits is its ability to convert textual and graphical information into precise geolocation data.

“Suppose you are aware of certain faults that have been occurring. Now you need to translate that to exactly which point machine on the permanent way is acting up,” he said, referring to the mechanical devices used to control and switch railway tracks.

Instead of technicians searching across hundreds of kilometres of track to find the faulty equipment, Jarvis allows them to pinpoint the exact location. “They go directly to the point machine that same night window and deal with it,” Ngien said. “It achieves better effectiveness, high productivity, and cost-savings.”

Despite the growing use of AI, Ngien stressed that the technology is meant to improve the effectiveness of SMRT’s workforce of over 10,000 people, not replace them.

“SMRT is still hiring, even in the face of this AI world. We still need engineers,” he said. “To us, AI is really about enabling the organisation to uplift our people.”

Jarvis is currently in its first phase of deployment, with over 50 SMRT engineers actively participating in the process. Some are analysing existing data, while others are involved in coding AI agents.

Ngien noted that managing a complex locomotive network, from signalling and power systems to railway tracks, requires a Kaizen culture of continuous improvement. “It’s a very challenging task, even for the engineers amongst us. But we have this culture to keep improving and make use of the tools available,” he said.

Chin added: “Rail operators depend on timely, accurate data to keep services running safely, reliably, and on schedule for millions of commuters each day. Running on OCI, Jarvis demonstrates how Oracle can help bring AI to where enterprise data resides to improve efficiency and operational responsiveness.”

Moving forward, Ngien said SMRT hopes to share its experience with other rail operators facing similar challenges. “They also have a trove of data, so through the models we’ve developed with [Oracle], we would be happy to share with other operators,” he said.