At Rise of AI 2019 in Berlin, Vlad Lata, Vice Chairman at KONUX, describes how his company built a predictive-maintenance product for railway switches. KONUX, then over 50 employees and having raised more than 50 million dollars, operates in Germany, Sweden and France and is expanding into Japan, China and the US. Lata frames the problem: in Germany alone 12 million passengers travel daily, one in five experiences a delay of more than six minutes, and half of passengers are dissatisfied, while maintenance rules date from the 1960s and 1970s and no longer match today's doubled train and cargo loads. He argues that building an AI product for such an industry requires three things together: domain expertise, IoT sensing, and AI, since existing railway systems were built for process control, not data analysis. KONUX attaches acceleration sensors to railway switches, converts the signal into vertical displacement to assess ballast condition, and applies predictive algorithms, following what Lata calls a strategic data acquisition cycle in which the product's immediate value to users drives more data and further product improvement.
Key takeaways
- KONUX builds AI predictive-maintenance products for railway switches, a physical component responsible for about 20 percent of all delay minutes.
- Vlad Lata argues that domain expertise, purpose-built IoT sensing and narrowly applied AI must combine, since legacy railway systems were built for process control, not data analysis.
- KONUX follows a strategic data acquisition cycle, attributed to Andrew Ng, where immediate customer value drives more data and further product improvement.
- A small neural network detects zero-crossings to correct sensor drift, converting acceleration traces into vertical displacement accurate to 0.1 millimeters versus laser measurement.
- Customers require at least a 90-day prediction horizon because a temporary intervention train costs 80,000 to 100,000 euros, and KONUX's hit rate has risen from about 90 to above 95 percent.
Detailed notes
KONUX's mission and the railway problem
Lata introduces KONUX, then just over 50 employees, having raised more than 50 million dollars including from a US fund (NEA), a Munich VC fund, and more recently a strategic investor, and operating across Germany, Sweden and France while expanding into Japan, China and the US. KONUX's mission is to empower the railway system to operate with the highest availability, because railway beats all other transport modes on emissions, accidents, noise and climate impact. He cites that in Germany alone 12 million passengers travel daily, one in five experiences a delay of more than six minutes, and about half of passengers are dissatisfied with service. He attributes part of the problem to maintenance rules and protocols designed in the 1960s and 1970s that no longer fit today's doubled volume of passenger and cargo trains.
Three keys to building an AI product: domain expertise, IoT and AI
Lata says KONUX's first key learning was that building an AI product in this space depends on three things: domain expertise to understand what should be predicted and what data matters; IoT, because existing railway systems were designed roughly 20 years ago for real-time process control, not for the deeper, more precise data needed for data analysis; and AI itself, applied narrowly. KONUX focused on railway switches, the physical point where a train changes track, which are influenced by many unrecorded variables such as weather, installation method, train speed and weight, and metal processing. Because historic data on these variables mostly does not exist, KONUX works with what Lata calls "small data which is kind of big data in disguise," combining sparse sensor data with heavy domain expertise.
The strategic data acquisition cycle
The second learning, which Lata attributes to Andrew Ng (who led the Google and Baidu AI teams), is to design a strategic data acquisition cycle: gather field data, build a product that creates immediate value for the customer, use that value to gain more users, and use more users to gain more data, feeding back into a better product. For KONUX this means installing acceleration sensors on switches, giving customers immediate failure predictions, and in return receiving customers' maintenance history and other sensor data. Lata says this creates a defensible position because only KONUX holds the resulting learning, while keeping the company focused on delivering short-term customer value.
How the sensor and prediction algorithm work
KONUX's device, built by manufacturing partners to KONUX's design, is mounted next to the switch blade on the rail track. As a train passes, it produces an acceleration trace, which is converted (via double integration) into vertical displacement, indicating the condition of the ballast, the stones fixing the switch in place. Data shows that good ballast condition reduces failure rate by a factor of six. Because double integration of noisy acceleration data causes signal drift, KONUX trained a small neural network to detect zero-crossings in the acceleration trace as recalibration points; the resulting displacement curve matches direct laser measurements to within 0.1 millimeters. KONUX also classifies train types from acceleration traces, first using a simple vector of axle-distance characteristics and later a neural network trained on known schedules, and can reconstruct a full train schedule over a switch after one day of data.
Key metrics and business impact
Lata says customers need at least a 90-day prediction horizon to plan the closures and lead processes required to fix a switch, since a temporary intervention train costs 80,000 to 100,000 euros to run. KONUX's hit rate started around 90 percent and is now above 95 percent. Switches cause about 20 percent of all delay minutes, so improving them directly improves punctuality; KONUX also aims to extend asset lifetime beyond fixed replacement cycles and to make maintenance more efficient, citing a case where it validated five different maintenance measures in two to three weeks versus a prior six-month wait-and-see cycle. KONUX monetizes as "system as a service," charging a yearly per-asset fee benchmarked against the savings its solution generates.
Q&A highlights
Asked how a rollout is validated before scaling across a whole network, Lata describes starting from a customer's historical failure data to estimate how many assets and how much time are needed, then discovering failures occurring sooner than the historical data suggested, which built a feedback cycle with the customer. Asked whether success would eventually remove KONUX's own training data by preventing failures, Lata says removing the system would mean losing field visibility entirely, and describes KONUX's longer-term ambition as becoming an operating system for the railway that also informs schedule planning and new route construction. Asked about using the roughly 50 million dollars raised, he says KONUX has stayed conservative with prior rounds, given that industry change cycles and deals can take two to four years, to ensure sufficient runway.
Quotes
We don't want to just apply AI generic, we want to look at specific components of a product and where can it be beneficial for us.
Vlad Lata
Questions answered in this talk
How does KONUX predict failures in railway switches?
According to Vlad Lata at Rise of AI 2019, KONUX attaches acceleration sensors to switches, converts the signal into vertical displacement to assess ballast condition, and applies predictive algorithms including a small neural network, reaching over 95 percent hit rate with a 90-day prediction horizon.
Why do railway switches matter for train punctuality?
Vlad Lata explains at Rise of AI 2019 that switches cause about 20 percent of all delay minutes on the railway, so improving their maintenance directly improves overall punctuality.
What is a strategic data acquisition cycle in an AI product?
At Rise of AI 2019, Vlad Lata describes a cycle, attributed to Andrew Ng, in which a product creates immediate value for users, which drives more users and data, which in turn improves the product and enables new products, calling this approach central to KONUX's growth.