Relying solely on historical SCADA (Supervisory Control and Data Acquisition) data for predictive maintenance has limitations due to data quality issues, the inability to capture all failure modes, the lack of external factors, and the challenges of dealing with evolving turbine technology. SCADA data is collected from sensors on wind turbines and includes parameters like wind speed, power output, temperature, and vibration. Predictive maintenance aims to forecast equipment failures and schedule maintenance proactively. Data quality issues are a significant limitation. SCADA data can be noisy, incomplete, or inaccurate due to sensor malfunctions, communication errors, or data processing issues. If the historical data is unreliable, any predictive model built on it will also be unreliable. The inability to capture all failure modes is another limitation. SCADA data typically focuses on easily measurable parameters but may not capture all the factors that contribute to equipment failure. For example, internal gearbox damage or blade cracks may not be directly detectable from SCADA data alone. The lack of external factors is a furt....
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