EOT has launched the ChronX platform to predict equipment failures. The AI system employs a contextual time-series transformer engine to analyse data from industrial machinery, such as pumps, compressors, and pressure sensors.

The system is designed for operational engineers to identify patterns that precede mechanical disruptions. Unlike traditional rule-based monitoring, ChronX learns the relationships between different operational variables to understand how failures develop over time. This allows for the prediction of the remaining useful life of equipment.

The software follows a three-step operational workflow: data preparation, model training using historical domain expertise, and real-time deployment for continuous monitoring. It is designed to integrate with existing industrial infrastructure, including SCADA systems, OPC-UA, and MQTT protocols.

ChronX can be deployed across on-premises, cloud, or hybrid environments. The platform aims to reduce the reliance on large-scale data science projects by allowing engineers to build and validate predictive models directly from live industrial data streams.

“Industrial operations already generate the signals that precede failures,” said Matt Oberdorfer, CEO of EOT. “ChronX understands equipment behaviour context in real time, enabling engineers to see patterns and prevent failures before they happen.”