AI 中文总结
研究针对风力涡轮机振动测量数据质量问题，提出多维熵指标，通过多视角表征信号分布捕捉差异，结合轻量级机器学习模型评估，在大规模异构数据集实验中识别错误测量准确率超99%，可作前端数据质量门保障后续分析。
AI 中文摘要
确保测量数据质量对于工业风力涡轮机传动系统的可靠状态监测至关重要，振动测量可能受传感器故障、涡轮机关闭状态等影响。本研究提出多维熵（MDE）指标作为振动测量有效性评估的前端数据质量评估和控制机制。通过多视角表征信号分布，MDE捕捉有效和错误振动测量间的统计差异。将MDE和RMS作为特征表示，用轻量级机器学习模型评估其有效性。在大规模异构真实数据集上的实验表明，该方法识别错误振动测量的准确率超99%。此方法可作为前端数据质量门，确保后续分析使用可靠振动测量，证明了MDE在异构传感器配置下的鲁棒性及其在工业规模振动测量质量评估中的潜力。
英文摘要
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.
Comments23 pages, 9 figs, 15 tables
