## 4 Results
### Data Collection and Preprocessing
These anomalies can originate from external factors like sensor noise and extreme weather disturbances, or internal factors such as pitch system failure, mechanical faults in the drivetrain, or shutdowns. To enhance the reliability of the gearbox fault warning model, two typical types of anomalous data need to be cleaned: The first type consists of clustered high-density outliers (clustered type), as shown in Figure 14(1), which are often caused by turbine shutdowns or sensor faults that cause inaccurate data recording, specifically when the wind speed is adequate, but the power output remains below the rated level. In such cases, the common approach is to delete these data points. The other type is randomly scattered isolated points (dispersed type), as shown in Figure 14(2),which are data values with abnormal distributions that differ significantly from other data points and exhibit unconventional trends. When such data constitutes a very small proportion, data quality can typically be improved by removing samples of variables that fall outside the normal range of variation. The wind speed-power curve after preprocessing is shown in Figure 15.  
(3) Sensor drift detection and cross-sensor data consistency verification  
During the data acquisition process, sensor measurement data may exhibit a slow drift phenomenon due to various factors. This drift mainly originates from three aspects: first, changes in the physical characteristics of the sensor itself, including component aging, mechanical structure relaxation, and material creep, which are internal factors; second, environmental interference, such as temperature fluctuations and electromagnetic interference,  
Figure 14: Distribution of wind speed–power curve anomalies. Figure 13: Ideal wind speed–power curve. which represent external influences; third, chemical corrosion, including oil contamination penetration, salt fog erosion, and other chemical reactions leading to performance degradation. These factors work together to cause the sensor output signal to gradually deviate from the true value, forming typical progressive and nonlinear drift characteristics. To address this issue, this study proposes a drift detection and compensation method based on the cumulative sum control chart (CUSUM). This method requires the establishment of a reliable reference benchmark: by collecting 5000 historical data samples from equipment operating in a healthy state, key statistical parameters, namely the mean and standard deviation, are calculated. By continuously monitoring the cumulative deviation of the data, significant sensor drift is identified when the cumulative sum statistics exceed the preset threshold for three consecutive sampling periods. Once sensor drift is detected, the exponentially weighted moving average (EWMA) filtering algorithm is used to dynamically correct the reference value, resetting the reference benchmark to effectively eliminate the drift interference in the data. The consistency issue of cross-sensor data primarily involves verifying the temporal consistency of the physical quantities of the collected data, ensuring that associated parameters comply with established physical laws, and confirming that constraint conditions are satisfied. To solve these problems, this study uses the comparison of sample timestamps to ensure that the data collection timestamps of different sensors are strictly synchronized, with the timestamp deviation controlled within 100 ms. Further, by verifying the threshold range of the collected samples and the association rules between different parameters, the rationality of the sample values is ensured. Through the above methods, cross-sensor data consistency verification is achieved.