基于声学信号的风力涡轮机轴承故障诊断的AI驱动方法
An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from Acoustic Signals
AI总结：
本研究提出一种基于卷积LSTM的AI方法，从声学信号中分类风力涡轮机轴承故障，测试准确率超99.5%，假阳性率低于1%，为风机维护提供支持。
摘要：
本研究旨在开发一种深度学习模型，用于从声学信号中对风力涡轮发电机中的轴承故障进行分类。利用五种预定义故障类型的音频数据进行训练和验证，成功构建并训练了一个卷积LSTM模型。为了创建数据集，采集了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表现出卓越的准确性，并在验证过程中展现出优秀的泛化能力，表明其具备良好的泛化性能。在测试样本上，模型实现了显著的分类性能，整体准确率超过99.5%，正常状态的假阳性率低于1%。本研究的发现为风力涡轮发电机轴承故障的诊断和维护提供了重要支持，并有望提高风力发电的可靠性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
