Hitachi Rail, a global railway infrastructure provider with 24,000 employees across 50+ countries, has partnered with NVIDIA to revolutionize rail operations through artificial intelligence. The collaboration resulted in HMAX (Hyper Mobility Asset Expert), an AI-powered digital asset management platform launched in September that’s already showing impressive results.
The partnership addresses critical challenges facing rail operators worldwide as passenger numbers continue rising. Giuseppe Marino, Group CEO of Hitachi Rail, explained that operators struggle with reducing maintenance costs, improving reliability, and extending train lifecycles. While Hitachi Rail had been using digital sensors to monitor transportation infrastructure—including train cars, rails, signaling systems, and tunnels—the company needed AI to accelerate data analysis.
The technical solution combines Hitachi’s existing sensor networks with NVIDIA’s IGX edge AI platform, enabling real-time data processing directly on trains rather than requiring manual data offloading at stations. Gajen Kandiah, President and COO of Hitachi Digital, emphasized the challenge: “We are capturing all of this data on the edge because the trains are rolling at all times, we have digital capabilities internally—how do we connect the two?”
The development process moved rapidly, with teams from Hitachi Rail, Hitachi Digital, and NVIDIA working together starting in February. HMAX integrates seamlessly with rail companies’ existing operations and maintenance systems, combining live sensor and camera data with AI algorithms to predict problems and optimize network performance.
The results have been remarkable. Over recent months, Hitachi installed HMAX on more than 2,000 train carriages, achieving a 20% reduction in service delays, 15% decrease in maintenance costs, and an impressive 40% cut in fuel costs at train depots. Previously, processing a single day’s worth of video footage took approximately 10 days—a lag time now eliminated through edge AI processing.
The platform monitors critical components like overhead power lines, motor temperatures, and vibration levels, enabling operators to identify cables needing repairs before disruptions occur. This predictive capability extends train longevity and reduces total ownership costs, allowing operators to replace trains less frequently.
Looking ahead, Hitachi Rail is expanding HMAX to additional operators, including Copenhagen Metro in Denmark, and exploring applications in other infrastructure-heavy industries like energy. The company continues enhancing the platform’s data collection capabilities and exploring new use cases.
Key Quotes
We needed to find a solution that leveraged the existing infrastructure. The question really became: We are capturing all of this data on the edge because the trains are rolling at all times, we have digital capabilities internally — how do we connect the two?
Gajen Kandiah, President and COO of Hitachi Digital and co-lead of the Hitachi AI Transformation Center, explained the core challenge that led to the NVIDIA partnership. This quote highlights the practical problem many industrial companies face: having data collection capabilities but lacking the AI infrastructure to analyze it in real-time.
We’re collecting a huge amount of information. With Nvidia, we can accelerate the way we’re applying artificial-intelligence systems to read any possible problem in a very smart way.
Giuseppe Marino, Group CEO of Hitachi Rail, described how the NVIDIA partnership transformed their data analysis capabilities. This statement underscores the shift from passive data collection to active AI-driven problem detection and prevention.
As much as we are doing, we’re learning. We are starting to see the potential of the product and of the technology — how do we start to solve different use cases?
Kandiah reflected on the ongoing evolution of HMAX and its expanding possibilities. This quote reveals that even with impressive early results, the companies view this as just the beginning of AI’s potential in infrastructure management.
Our Take
What’s particularly compelling about this implementation is the speed and scale of measurable impact. Many AI deployments struggle to demonstrate clear ROI, yet Hitachi Rail achieved double-digit percentage improvements across multiple metrics within months of deployment on 2,000+ carriages. This success stems from addressing a well-defined problem with existing data infrastructure rather than attempting to build AI solutions from scratch.
The edge AI approach is especially noteworthy—processing data where it’s generated eliminates the 10-day lag that previously existed, transforming reactive maintenance into predictive prevention. This architectural decision could become a blueprint for other industries managing distributed physical assets.
The partnership model itself offers lessons for AI adoption: established industrial players bringing domain expertise and data, combined with specialized AI companies providing computational platforms. As Hitachi explores energy sector applications, we’re likely seeing the emergence of horizontal AI platforms that can adapt across infrastructure-heavy industries, potentially creating significant competitive advantages for early adopters.
Why This Matters
This partnership represents a significant milestone in AI’s practical application to critical infrastructure, demonstrating how edge computing and machine learning can solve real-world operational challenges at scale. The 20% reduction in service delays and 40% fuel cost savings aren’t incremental improvements—they’re transformative results that could reshape the economics of public transportation globally.
The collaboration highlights an important trend: established industrial companies partnering with AI technology leaders to modernize legacy systems without complete infrastructure overhauls. By leveraging existing sensor networks with NVIDIA’s edge AI capabilities, Hitachi Rail created a scalable solution applicable across industries managing distributed physical assets.
For the broader AI industry, this case study validates the edge AI computing model, where processing happens locally rather than in centralized data centers. This approach solves latency issues critical for real-time decision-making in transportation, manufacturing, and energy sectors. As rail passenger numbers increase worldwide and infrastructure ages, AI-driven predictive maintenance becomes essential for sustainable urban development. The expansion to Copenhagen Metro and potential energy sector applications suggests this technology could become standard across infrastructure management, creating a substantial market for industrial AI solutions.
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Source: https://www.businessinsider.com/hitachi-and-nvidia-ai-tech-improve-rail-infrastructure-2024-11