Project description

SWIFT develops and validates a sensor-based, data-driven monitoring framework for wind turbine main bearings and gearbox bearings. The project combines edge sensing, vibration analysis, and artificial intelligence to detect and predict bearing faults even when full OEM data or design details are unavailable.

The work is structured in five technical work packages:

• WP1 – Low-cost sensor technology and data acquisition: design and validation of an add-on sensing setup capable of capturing relevant vibration and operational data from turbines.
• WP2 – Failure annotation and reference database: creation of a standardized dataset linking field observations, vibration signatures, and failure cases.
• WP3 – Blind vibration-based diagnostics: development of analysis methods that can identify bearing faults without requiring confidential OEM models or geometry.
• WP4 – Fleet-level SCADA anomaly detection: extraction of health indicators and anomaly patterns from existing SCADA data across multiple turbines and sites.
• WP5 – Data fusion and decision support: integration of the developed algorithms into a hybrid framework that combines vibration and SCADA insights to generate actionable maintenance recommendations.

The SWIFT methodology will be validated using data from the Norther offshore wind farm, serving as an industrial case study.