Multi-Dimensional Entropy for Vibration Measurement Data Quality Assessment and Erroneous Signal Identification in Wind Turbines

Abstract

Ensuring measurement data quality is essential for reliable condition monitoring of industrial wind turbine drivetrains, where vibration measurements can be affected by sensor malfunctions, turbine shutdown conditions, and other non-diagnostic states. Such invalid measurements may compromise the reliability of subsequent monitoring and data-driven analysis procedures. This study proposes a Multi-Dimensional Entropy (MDE) metric as a front-end data quality assessment and control mechanism for vibration measurement validity evaluation. By characterizing signal distributions from multiple perspectives, including time-domain amplitude, spectral amplitude, and frequency-band energy, MDE captures statistical differences between valid and erroneous vibration measurements. By integrating MDE and RMS as feature representations, lightweight machine learning models are employed as evaluation tools to assess the effectiveness of the proposed representation. Experiments on a large-scale, heterogeneous real-world dataset comprising 57,643 vibration samples collected from 12 wind farms and 14 turbine units, covering multiple drivetrain components, diverse sensor brands, and varying sampling configurations over long-term operation, demonstrate that the proposed method achieves over 99 percent accuracy in identifying erroneous vibration measurements. The proposed approach can be deployed as a front-end data quality gate before downstream signal processing, feature extraction, and condition monitoring procedures, ensuring that subsequent analyses are performed using reliable vibration measurements. The results demonstrate the robustness of MDE under heterogeneous sensor configurations and highlight its potential for industrial-scale vibration measurement quality assessment.