【摘要】 针对水轮机在发生空化时，其声发射信号的时频域特征相较于正常时变化不大而导致故障诊断模型精度不高的问题，提出一种基于谱减法与卷积神经网络（CNN）的融合诊断方法。首先，通过谱减法对空化与正常信号进行双信号互减，实现信号的差异化增强，显著提升频段特征的可分性。再把处理好的正常信号与空化信号作为样本，输入轻量化CNN模型中进行训练，最终得到水轮机空化故障诊断结果。为验证方法可行性，采用混流式水轮机顶盖与蜗壳测点的多工况声发射数据进行实验，结果表明：所提模型的平均诊断准确率达99.56%（顶盖）和99.81%（蜗壳），较其他几种信号处理方法提升显著，并且所提模型的准确率以及计算效率更高。该方法兼具较高的精度与适应性，为水轮机空化监测提供了一种有效的参考方案。更多还原

【Abstract】 Based on spectral subtraction and convolutional neural networks(CNN), this study develops a fusion diagnostic method to improve previous fault diagnostic model with low accuracy in the time and frequency domain characteristics of acoustic emission signals from a hydraulic turbine experiencing cavitation. Firstly, a differential enhancement of the signals through dual-signal mutual subtraction of the cavitation and normal signals was achieved by using spectral subtraction, which significantly improved the separability of band features. Then, samples of the processed normal and cavitation signals were input into a lightweight CNN model for training, and finally the turbine cavitation fault diagnosis results were obtained. To verify this method, we conduct a numerical experiment using multi-case acoustic emission data from the measuring points on the top cover and spiral case of a Francis turbine. The results showed an average diagnostic accuracy as high as 99.6% at top cover and 99.8% at spiral case, which was a significant improvement over several other signal processing methods with accuracy, adaptability and computational efficiency enhanced, putting forward an effective scheme for hydraulic turbine cavitation monitoring.更多还原