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.

翻译：本研究旨在开发一种基于深度学习的模型，用于从声学信号中对风力发电机轴承故障进行分类。通过使用五种预定义故障类型的音频数据进行训练和验证，成功构建并训练了卷积长短期记忆网络（LSTM）模型。为构建数据集，采集原始音频信号数据并进行分帧处理，以捕获时域和频域信息。该模型在训练样本上展现出卓越的准确率，并在验证过程中表现出优异的泛化能力，证明了其良好的泛化性能。在测试样本上，模型实现了显著的分类性能，总体准确率超过99.5%，正常状态下的假阳性率低于1%。本研究结果为风力发电机轴承故障的诊断与维护提供了重要支持，有望提升风力发电的可靠性与效率。