Modern wind turbines employ a multistage planetary gearbox to convert the low rotation speed of the turbine blades to the speed required by the generator. Studies have shown that gearbox failures rank the highest among the contributors to an unplanned downtime. Real-time condition monitoring systems can provide useful insights to a turbine’s operation there by reducing the chance of an unplanned downtime. This study focused on developing an automated real-time fault detection methodology for a miniature wind turbine planetary gearbox subjected to non-stationary loading. The data-driven multi-component fault detection methodology implements multiple scales of continuous wavelet transform to extract information from a non-stationary signal. This multi-scale approach ensures that all possible component signatures are captured and organized into a feature rich data-set. The wavelet coefficients were then abstracted using descriptive statistics to reduce size of data-set. This was done so as to minimize the computation requirements. The proposed methodology was tested using a pattern recognition algorithm based on Artificial Neural Networks and two Decision Tree algorithms. The results indicated that the proposed methodology worked well with the Decision Tree algorithm thereby ensuring that such a method could be deployed for a compact signal analyzer, where processing capability and memory capacity is premium. Further, a stand-alone application was deployed to automate the process with the trained machine learning model. The proposed method proved its capability in classifying multi-component faults under non-stationary operating conditions.

现代风力涡轮机采用多级行星齿轮箱将涡轮叶片的低转速转换为发电机所需的速度。研究表明，变速箱故障在导致计划外停机的原因中排名最高。实时状态监测系统可以通过减少意外停机的机会为涡轮机的运行提供有用的见解。这项研究的重点是为承受非固定载荷的微型风力涡轮机行星齿轮箱开发一种自动实时故障检测方法。数据驱动的多分量故障检测方法实施多尺度连续小波变换以从非平稳信号中提取信息。这种多尺度方法可确保捕获所有可能的组件签名并将其组织到功能丰富的数据集中。然后使用描述性统计提取小波系数以减少数据集的大小。这样做是为了最小化计算要求。使用基于人工神经网络的模式识别算法和两种决策树算法对所提出的方法进行了测试。结果表明，所提出的方法与决策树算法配合良好，从而确保可以将这种方法部署到紧凑型信号分析仪，其中处理能力和存储容量是优质的。此外，还部署了一个独立的应用程序，以使用经过训练的机器学习模型自动执行该过程。