Operation Status Diagnosis of an Underwater Cutting Robot Using Adaptive Weighted Parallel 1D-DenseNet

The interior of a nuclear reactor, filled with water and classified as a medium-level radiation area, is inaccessible to humans, requiring underwater remote cutting during decommissioning. However, the cutting process generates bubbles and light, hindering camera-based monitoring and necessitating status determination through sensor data. This study introduces an adaptive weighted parallel 1D-DenseNet that integrates pressure and hydrophone sensor data in both time and frequency domains to distinguish between cutting and idle states. Time-series data are transformed into the frequency domain via fast Fourier Transform (FFT), generating four inputs: raw and FFT signals for each sensor. These inputs are processed through a parallel network, where the outputs of 1D-DenseNet are multiplied by adaptive weights, concatenated, and passed through a fully connected layer for status determination. The proposed method achieves higher computational efficiency than conventional time-frequency approaches and seamlessly integrates additional sensors. Experimental results on a validation dataset show an accuracy of 98.85% and an F1-score of 0.9888. Comparisons with baseline models and an ablation study confirm its superior performance. The proposed model offers an effective solution for monitoring underwater cutting processes during nuclear reactor decommissioning