EOT has launched a predictive operations system to help industrial engineers detect equipment failures before they occur. The ChronX platform uses a contextual time-series transformer AI engine to learn operational behaviours directly from industrial data streams.

The system is designed to move beyond traditional dashboards and static alarms. By analysing the relationships between variables such as pressure, temperature, and flow, ChronX identifies patterns that precede downtime. It provides engineers with predictions on remaining useful life (RUL) and recommendations for intervention timing.

The workflow involves three stages: preparing and normalising data, training models using historical expertise, and deploying “guards” to monitor live equipment. The platform is intended to be used by operational engineers without requiring extensive data science or coding knowledge.

“Industrial operations already generate the signals that precede failures — but most engineers only see them after operational impact has already begun,” said Matt Oberdorfer, CEO of EOT.

ChronX supports deployment across on-premises and cloud infrastructures. It integrates with existing industrial systems, including SCADA, MQTT, and OPC-UA, to ensure compatibility with established factory historians.