AI 中文总结
该研究提出面向部署的多维熵（MDE）方法，经大量工业振动数据实验验证，其可作为轻量特征层提升风电监测可靠性，降低误差传播风险。
AI 中文摘要
传感器故障、停机瞬态及异常采集条件导致的错误振动信号会降低自动化工业监测流程的可靠性。本文针对风力发电机振动数据质量控制，开展面向部署的多维熵（Multi-Dimensional Entropy，MDE）分析，重点关注计算效率、模型无关性、物理可解释性及鲁棒性。对来自12个风电场14台机组的57643条带标签工业振动记录（涵盖主轴承、齿轮箱及发电机）进行实验，结合3个额外风电场4152条未见过记录的跨平台部署验证与跨机组泛化测试，结果表明MDE在不同分类器及异构运行条件下可提供稳定且具判别性的特征表示，同时保持较低的计算与内存需求。这些结果证明MDE可作为轻量且可直接部署的振动数据质量控制特征层，从而提升工业监测流程的可靠性，降低误差向风能应用下游诊断与预测任务传播的风险。
英文摘要
Erroneous vibration signals caused by sensor malfunction, shutdown transients, and abnormal acquisition conditions can degrade the reliability of automated industrial monitoring pipelines. This paper presents a deployment-oriented analysis of Multi-Dimensional Entropy (MDE) for vibration data quality control in wind turbines, focusing on computational efficiency, model-agnostic capability, physical interpretability, and robustness. Experiments on 57,643 labeled industrial vibration records from 12 wind farms and 14 turbine units, covering main bearings, gearboxes, and generators, together with cross-platform deployment validation and cross-turbine generalization tests on 4,152 unseen records from 3 additional wind farms, show that MDE provides a stable and discriminative feature representation across different classifiers and heterogeneous operating conditions while maintaining low computational and memory requirements. These results demonstrate that MDE can serve as a lightweight and deployment-ready feature layer for vibration data quality control, thereby improving the reliability of industrial monitoring pipelines and reducing the risk of error propagation into downstream diagnostic and prognostic tasks in wind energy applications.
Comments10 pages, 2 figures, 11 tables
