Broken Wire Detection Based on TDFWNet and Its Application in the FAST Project

This research proposes a wire-breakage detection method based on a Time-Domain Feature Weighted Network (TDFWNet) to address the challenging issue of wire-breakage detection in the feed source cabin drive cables of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The study begins with a temporal domain morphology analysis, revealing significant differences between wire-breakage signals and interference signals in key characteristic parameters such as waveform factor, pulse factor, and kurtosis. These parameters are thus employed as the basis for feature input, and their corresponding feature probabilities are calculated to provide prior feature weights for the model. The TDFWNet model integrates the feature learning capability of a Convolutional Neural Network (CNN) with temporal domain feature analysis using the feature probabilities derived from key temporal domain characteristic parameters as weight inputs to enhance the sensitivity and recognition accuracy of wire-breakage signals. Furthermore, the research team has developed a data augmentation method based on Feature-Constrained Dynamic Time Warping (FCDTW). This method processes the original wire-breakage signals to generate high-quality augmented data, thereby improving the model’s ability to recognize wire-breakage signals. Ultimately, the TDFWNet outperforms traditional CNN models by 1.5%, 2.0%, 1.8%, and 16.6% in precision, recall, F1 score, and accuracy, respectively. In practical engineering applications, this method demonstrated excellent stability and practicality in three domestic FAST drive cable-bending fatigue tests. The detected suspected wire-breakage signals were highly consistent with the results of post-fatigue test disassembly inspections, effectively supporting the wire-breakage detection requirements in actual engineering scenarios.