Sound-Based Intelligent Detection of FOD in the Final Assembly of Rocket Tanks

The traditional method of relying on human hearing to detect foreign object debris (FOD) events during rocket tank assembly processes has the limitation strong reliance humans and difficulty in establishing objective detection records. This can lead undetected FOD entering engine with fuel causing major launch accidents. In this study, we developed an automatic, intelligent system for tanks based sound signals overcome drawbacks manual detection, enabling us take action prevent accidents advance. First, used log-Mel transformation reduce high sampling rate signal. Furthermore, proposed a multiscale convolution temporal convolutional network (MS-CTCN) challenges multi-scale feature extraction suspicious events. Finally, post-processing strategies label smoothing threshold discrimination refine results event ultimately determine presence FOD. was validated through experiments. showed that had accuracy 99.16% detecting better potential compared baseline method.