Abnormal-Sound Diagnosis for Kaplan Hydroelectric Generating Units Based on Continuous Wavelet Transform and Transfer Learning

To realize abnormal-sound diagnosis in hydroelectric generating units, this study proposes a method based on continuous wavelet transform (CWT) and Transfer Learning (TL). A denoising algorithm utilizing spectral noise-gate technology is proposed to enhance fault characteristics in hydroelectric units. Subsequently, Continuous Wavelet Transform is applied to obtain frequency components, and the results are converted into a series of pseudo-color images to highlight information differences. A transfer model is subsequently developed for feature extraction, utilizing simplified fully connected layers to reduce modeling costs. The study optimizes key parameters during the signal-processing stage and achieves an improved parameter-setting scheme. Acoustic signals corresponding to four different fault states and a normal state are collected from a Kaplan hydroelectric generating unit in a hydropower station. The signal diagnosis accuracy rates before filtering are 84.83% and 95.14%. These rates significantly improved to 98.88% and 98.06%, respectively, demonstrating the effectiveness of the noise-reduction process. To demonstrate the superiority of the improved model in this work, a series of classic deep-learning models, including AlexNet, Resnet18, and MobileNetV3, are used for comparative analysis. The proposed method can effectively diagnose faults in Kaplan hydroelectric generating units with a high accuracy, which is crucial for the daily monitoring and maintenance of these units.