This study aimed to develop a deep learning model for the classification of
bearing faults in wind turbine generators from acoustic signals. A
convolutional LSTM model was successfully constructed and trained by using
audio data from five predefined fault types for both training and validation.
To create the dataset, raw audio signal data was collected and processed in
frames to capture time and frequency domain information. The model exhibited
outstanding accuracy on training samples and demonstrated excellent
generalization ability during validation, indicating its proficiency of
generalization capability. On the test samples, the model achieved remarkable
classification performance, with an overall accuracy exceeding 99.5%, and a
false positive rate of less than 1% for normal status. The findings of this
study provide essential support for the diagnosis and maintenance of bearing
faults in wind turbine generators, with the potential to enhance the
reliability and efficiency of wind power generation.

开发了一个用于风力涡轮机发电机轴承故障分类的深度学习模型，通过使用来自五种预定义故障类型的音频数据进行构建和训练。该模型在训练样本上表现出色，验证过程中也展示出优秀的泛化能力，对于测试样本，模型的分类性能出色，整体准确率超过 99.5%，正常状态的误报率低于 1%。该研究的发现对于风力发电的故障诊断和维护提供了重要支持，并具有提高风力发电的可靠性和效率的潜力。