CETO Innovation provides an AI‑driven predictive maintenance platform for district heating networks, using mobile in‑pipe probes with high‑resolution sensors to detect leaks, corrosion and structural defects in real time. The collected data is analyzed by machine‑learning models to forecast failures, generate maintenance recommendations, and create digital twins that integrate with existing GIS and utility IT systems, helping operators reduce downtime and maintenance costs.
Funding
Funding not disclosed
Founders
Product
Problem
District heating operators rely on periodic, often invasive inspections and reactive maintenance to identify leaks, corrosion, and material degradation in underground pipelines. These methods are costly, disruptive, and can miss early-stage failures, leading to unplanned outages, higher repair expenses, and reduced network lifespan.
Solution
CETO Innovation offers an AI-driven predictive maintenance platform tailored for district heating networks. The solution combines advanced in‑pipe probes equipped with high‑precision sensors that travel through active pipelines to detect leaks, corrosion, and structural defects in real time. Collected sensor data is streamed to a centralized analytics hub where machine‑learning models analyze patterns, forecast potential failures, and generate actionable maintenance recommendations. The platform also provides detailed infrastructure mapping and integrates with existing GIS and IT systems, giving operators a comprehensive, data‑rich view of pipeline health. By shifting from reactive to proactive asset management, utilities can minimize downtime, lower maintenance costs, and extend the service life of their heating infrastructure.
Target Audience
Primary customers are district heating utility operators and infrastructure managers responsible for the maintenance and reliability of large‑scale underground heating networks.
Features
- Mobile in‑pipe probe with high‑resolution sensors for real‑time detection of leaks, material degradation, and corrosion inside active pipelines (≥60 mm diameter)
- AI‑powered predictive analytics that continuously learn from real‑time and historical sensor data to forecast failures and optimize maintenance schedules
- Automated infrastructure mapping that creates accurate digital twins of underground heating networks for seamless GIS integration
- Centralized data hub delivering dashboards, alerts, and 3D visualizations to support informed decision‑making and risk assessment
- Compatibility with existing utility IT and GIS platforms through open APIs, enabling easy data exchange and workflow integration