Industrial Equipment Failure Prediction
Machine learning models analyze industrial sensor data, detect signs of impending failure, and estimate time to failure. The system helps schedule maintenance before emergency shutdowns.
The task
Predict failures of industrial equipment from sensor data and estimate the time remaining before they occur, so maintenance can be planned ahead of an emergency shutdown.
Telemetry and models
The system processes time series of vibration, temperature, current and acoustics from pumps, centrifuges, electric motors and other rotating equipment.
We developed models of normal operation, anomaly detection and failure prediction. The analysis accounts for how indicators evolve and for combinations of several signals, and data preparation includes quality control.
The engineer’s view
The system shows which unit needs attention, which signals have changed and how the deviation is expected to develop. The estimated time to a possible failure is used when planning diagnostics and maintenance.
A stream of telemetry becomes information about the condition of specific equipment and the expected changes in its operation.
Interface demonstration
Three screens follow the workflow from fleet monitoring through equipment diagnostics to maintenance planning. This public reconstruction uses synthetic data and does not disclose the customer’s equipment or metrics.