Research on wavelet packet energy entropy extraction method for acoustic signal of tunnel lining cavity

Abstract Tunnel lining cavity is a common disease in high-speed railway tunnels, which seriously affects the safe operation of the line. At present, the commonly used method is manual knocking inspection, but it relies on manual judgment, subjective factors have great influence, and there are errors. This paper proposes a feature extraction method of acoustic vibration signal of tunnel lining cavity based on wavelet packet energy entropy and uses wavelet packet energy entropy to realize the identification of cavity defects. The full-scale simulation model of tunnel with a radius of 6 m was constructed by COMSOL and different cavity conditions were set up. The wavelet packet energy entropy characteristics of acoustic signals were extracted and analyzed. The mapping relationship between cavity depth and cavity size and wavelet packet energy entropy was explored. The wavelet packet energy entropy and frequency band distribution characteristics were extracted to realize the identification of cavity defects. The local model which can characterize the whole tunnel is designed and the embedded defects are verified by experiments. The results show that the wavelet packet energy entropy can be used as the identification feature of tunnel lining cavity defects. When the wavelet packet energy entropy is less than 2.53, the existence of tunnel lining cavity defects is characterized. When the wavelet packet energy entropy is in the range of 2.53–2.94, it characterizes the existence of the transition boundary between tunnel lining cavity defect and no disease. This index can be used to realize the rapid and effective identification of cavity defects.