This paper, written by SAS' Jagdishwar Mankala, Bahar Biller, and Tom Anderson, presents a conceptual predictive maintenance framework that could be developed using SAS software to enhance wind farm management through real-time anomaly detection in turbine performance. Leveraging a combination of sensor measurements, event stream processing, and machine learning, the framework demonstrates how real-time asset monitoring and predictive analytics can be achieved in a plug-and-play configuration compatible with equipment from different vendors. For utility companies, such an approach can reduce unplanned downtime and improve operational efficiency. It also represents a viable path toward managing the entire asset lifecycle, enhancing maintenance scheduling, and optimizing inventory levels. The framework described here could be implemented using SAS® Viya® (or SAS® Viya® Workbench) and SAS® Event Stream Processing software and could be packaged for deployment across utility infrastructures. The paper aims to guide the reader through the key steps involved in developing such a system.
