This invention relates to the field of
hydropower technology, specifically to a method for diagnosing acoustic faults in
hydro turbines based on CRK-enhanced CNN-GRU and dual-channel self-attention. The method includes: S1 deploying
noise sensors near the
turbine runner to collect acoustic signals that avoid high-frequency interference; S2 segmenting the
signal into 1024 data points per segment, analyzing the
signal using
Fast Fourier Transform (FFT) to extract time-frequency features such as peak amplitude and energy proportion in the 400-600Hz
frequency band; S3 constructing CRK based on a Tent
chaotic mapping-improved Runge-Kutta optimization
algorithm to enhance global search capability and convergence speed; S4 constructing a CNN-GRU-dual-channel self-
attention model to sequentially achieve local time-frequency
feature extraction, temporal dynamic dependency modeling, and multi-dimensional feature enhancement; S5 optimizing model hyperparameters using CRK; and S6 training the model by dividing it into training and test sets, employing transfer learning when samples are scarce. This invention exhibits strong anti-interference capabilities, efficient
hyperparameter optimization, and can accurately diagnose normal, early, and extended crack states of the
turbine runner. It is suitable for scenarios with scarce samples and has high
engineering practicality.