Turning raw sensor data into actionable decisions is a major challenge in industrial asset management. This session walks through how vibration data from wind turbines can be transformed into reliable health indicators and remaining useful life (RUL) predictions using SAS Viya. By combining rigorous data quality checks, signal processing, and statistical modeling, the approach reduces false positives and enables more confident maintenance decisions. The result is an operational workflow that scales across turbines and sites while improving asset reliability and reducing unplanned downtime.
Presenters: Andrew Brody and Tom Anderson, SAS
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