Image-Based Multi-Domain Feature Extraction for IoUT-Oriented Monitoring of Hydrofoil Tip Leakage Flow Cavitation

For Internet of Underwater Things (IoUT)−oriented condition monitoring of underwater propulsion systems, continuous visual observations can provide rich information on the spatial distribution and temporal evolution of cavitation, while the direct transmission of complete image sequences may impose a substantial data burden on underwater communication networks. To provide compact and physically interpretable visual monitoring information, this study develops an image-derived multi-domain feature−extraction method for hydrofoil cavitation monitoring. Cavitation image sequences of an original NACA0009 hydrofoil and a hydrofoil with hole−pit structures were analyzed as two experimental configurations exhibiting different cavitation behaviors. Static−background subtraction, RGB−channel−response processing, Canny edge detection, morphological processing, and threshold-based focusing were applied to extract cavitation regions. The normalized cavitation−area time series of the tip leakage vortex (TLV), tip separation vortex (TSV), and six local regions were then constructed from consecutive images and treated as image-derived monitoring signals. Time−domain and spatial features were used to characterize the overall cavitation level and regional distribution, while Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) were further employed to describe spectral fluctuations and multi-scale transient variations. The extracted monitoring features showed clear responses to changes in hydrofoil configuration and operating conditions. Under the baseline condition, the mean TLV and TSV cavitation−area features of the hydrofoil with hole−pit structures were approximately 40.0% and 85.0% lower than those of the original hydrofoil, respectively. Differences were also observed in the regional, frequency−domain, and time−frequency characteristics and remained observable under the two changed operating conditions. These results indicate that continuous cavitation images can be transformed into complementary multi-domain descriptors for characterizing variations in cavitation behavior. The proposed approach therefore provides a potential visual sensing and feature−extraction component for future IoUT-based condition monitoring of underwater propulsion systems, while reducing reliance on the transmission of complete image sequences.