Fan Kesong, Yan Ao, Liu Shaowei, Zhang Can, Feng Youliang, Fu Mengxiong, He Deyin
School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, Hean, China.
State Key Laboratory of Coal Mine Safety Technology, CCTEG Shenyang Research Institute, Shenfu Demonstration Zone, Liaoning, 113122, China.
Sci Rep. 2025 Apr 30;15(1):15239. doi: 10.1038/s41598-025-98739-w.
Currently, the ultrasonic guided wave inspection of the anchoring quality of rebar resin bolts in coal mine tunnels faces issues such as rapid signal energy attenuation, waveform superposition of reflected waves, and waveform complexity, making it difficult to effectively identify anchoring quality. Therefore, a method combining Empirical Mode Decomposition (EMD) with Principal Component Analysis (PCA) is proposed for processing and analyzing ultrasonic guided wave detection signals. This study employs numerical simulation combined with laboratory testing, selecting low-frequency guided waves at 50 kHz with lower attenuation as excitation signals to simulate the propagation process of ultrasonic guided waves through rebar resin bolt and surrounding anchorage media. Concurrently, an indoor nondestructive testing experimental platform was established to investigate signal propagation patterns across specimens with varying anchorage qualities. Finally, the EMD-PCA method was applied to process and analyze defect signals. Results demonstrate that this signal processing method can accurately identify the actual positions and lengths of anchorage defects, with discrepancies between numerical simulations and laboratory measurements remaining within 9.5%. For extended anchorage defects, the positioning error of defect locations is less than 2%. When defect interfaces approach the distal end of the anchorage, the IMF2 mode derived from EMD decomposition remains effective in extracting wave impedance difference interface reflections, thereby verifying the feasibility of applying the EMD-PCA signal processing method to ultrasonic guided wave nondestructive detection of defects in rebar resin bolts in coal mine tunnels.
目前,煤矿巷道中钢筋树脂锚杆锚固质量的超声导波检测面临信号能量快速衰减、反射波波形叠加以及波形复杂等问题,难以有效识别锚固质量。因此,提出了一种将经验模态分解(EMD)与主成分分析(PCA)相结合的方法来处理和分析超声导波检测信号。本研究采用数值模拟与实验室测试相结合的方式,选择衰减较低的50kHz低频导波作为激励信号,模拟超声导波在钢筋树脂锚杆及周围锚固介质中的传播过程。同时,搭建了室内无损检测实验平台,研究不同锚固质量试件上信号的传播规律。最后,应用EMD-PCA方法对缺陷信号进行处理和分析。结果表明,该信号处理方法能够准确识别锚固缺陷的实际位置和长度,数值模拟与实验室测量结果的差异在9.5%以内。对于扩展锚固缺陷来说,缺陷位置的定位误差小于2%。当缺陷界面靠近锚固远端时,EMD分解得到的IMF2模态仍能有效提取波阻抗差异界面反射信号,从而验证了将EMD-PCA信号处理方法应用于煤矿巷道钢筋树脂锚杆缺陷超声导波无损检测的可行性。