Page 255 - 《应用声学》2025年第1期

第 44 卷 第 1 期 龙士国等： 基于 LabVIEW 的智能建筑外墙饰面砖脱空识别软件开发 251

基于声音信号分析的脱空识别方法，结合首波幅值 pressure and flow monitoring[J]. Measurement, 2022, 190:
和小波系数积分比特征，经拉依达准则预处理后的 110705.
[9] Nithyaa A N, Premkumar R, Dhivya S, et al. A real
数据运用 KNN算法构建识别模型。在此基础上，开
time foot pressure measurement for early detection of ul-
发了智能建筑外墙饰面砖脱空识别软件，对外墙试 cer formation in diabetics patients using labview[J]. Pro-
件进行饰面砖脱空实验验证其性能，实验结果表明， cedia Engineering, 2013, 64: 1302–1309.
脱空识别率达到 92%。该软件界面直观，操作简单， [10] 张国才, 游泳, 沈洋, 等. 脉冲超声换能器声场测试系统的设
计 [J]. 应用声学, 2020, 39(6): 876–884.
表现出良好的运行性能和较高的可靠性，可在实际
Zhang Guocai, You Yong, Shen Yang, et al. De-
工程应用中帮助快速准确地识别饰面砖脱空。脱空 sign of sound field testing system of pulsed ultrasonic
边界仍存在一定的误判率，未来的研究将优化分类 transducer[J]. Journal of Applied Acoustics, 2020, 39(6):
算法与模型，以提高脱空边界的识别准确率。 876–884.
[11] Bo L, Liu X F, He X X. Measurement system for wind
turbines noises assessment based on LabVIEW[J]. Mea-
参 考 文 献 surement, 2011, 44(2): 445–453.
[12] 向博伦. 基于冲击弹性波法检测混凝土结构抗冻性研究 [D].
[1] 王璞瑾, 肖建庄, 段珍华, 等. 建筑物外立面损伤检测智能化 大连: 大连理工大学, 2021.
发展趋势 [J]. 建筑科学与工程学报, 2022, 39(4): 24–37. [13] Sun D Y, Goktogan A J. Tiled-facade condition assess-
Wang Pujin, Xiao Jianzhuang, Duan Zhenhua, et al. In- ment using fourier and wavelet features of impact-acoustic
telligent development trend of building enclosure damage signals and support vector machine[C]//Australasian
detection[J]. Journal of Architecture and Civil Engineer- Conference on Robotics and Automation, 2018.
ing, 2022, 39(4): 24–37. [14] Yao F, Chen G, Abula A. Research on signal processing
[2] 赵舒祎, 胡长明, 张宏丽. 基于组合赋权云模型的外墙瓷砖脱 of segment-grout defect in tunnel based on impact-echo
落因素分析 [J]. 工业安全与环保, 2023, 49(9): 6–10. method[J]. Construction and Building Materials, 2018,
Zhao Shuyi, Hu Changming, Zhang Hongli. Analysis of 187: 280–289.
shedding factors of exterior wall tiles based on combined [15] 姚菲, 陆幸奇, 陈光宇. 基于冲击回波法的混凝土 -围岩缺陷
weighting cloud model[J]. Industrial Safety and Environ- 检测与信号处理研究 [J]. 铁道科学与工程学报, 2021, 18(9):
mental Protection, 2023, 49(9): 6–10. 2316–2323.
[3] Soeta T, Ito S, Fujinuma T, et al. Trial inspection Yao Fei, Lu Xingqi, Chen Guangyu. Experimental and
of exterior-tile wall specimens with a prototyped tile- signal processing research on concrete-rock structural de-
debonding diagnostic device[J]. Japan Architectural Re- fects by impact-echo method[J]. Journal of Railway Sci-
view, 2021, 4(1): 28–40. ence and Engineering, 2021, 18(9): 2316–2323.
[4] Inoue F, Doi S, Ishizaki T, et al. Study on automated in- [16] Kang S, Yu J D, Hong W T, et al. Estimation of cavities
spection robot and quantitative detection of outer tile wall beneath plate structures using a microphone: Laboratory
exfoliation by wavelet analysis[C]// International Confer- model tests[J]. Sensors, 2021, 21(9): 2941.
ence on Control, Automation and System. IEEE, 2010: [17] 周欣磊, 顾海挺, 刘晶, 等. 基于集成学习与深度学习的日
994–999. 供水量预测方法 [J]. 浙江大学学报 (工学版), 2023, 57(6):
[5] Luk B L, Liu K P, Tong F, et al. Impact-acoustics in- 1120–1127.
spection of tile-wall bonding integrity via wavelet trans- Zhou Xinlei, Gu Haiting, Liu Jing, et al. Daily wa-
form and hidden Markov models[J]. Journal of Sound and ter supply prediction method based on integrated learn-
Vibration, 2010, 329(10): 1954–1967. ing and deep learning[J]. Journal of Zhejiang Univer-
[6] Sugimoto T, Sugimoto K, Uechi I, et al. Efficiency im- sity(Engineering Science), 2023, 57(6): 1120–1127.
provement of outer wall inspection by noncontact acous- [18] Sugimoto K, Akamatsu R, Sugimoto T, et al. Defect-
tic inspection method using sound source mounted type detection algorithm for noncontact acoustic inspection
UAV[C]// International Ultrasonics Symposium. IEEE, using spectrum entropy[J]. Japanese Journal of Applied
2019: 2091–2094. Physics, 2015, 54(7S1): 07HC15.
[7] 刘素贞, 饶诺歆, 张闯, 等. 基于 LabVIEW 的电磁超声无损检 [19] Taunk K, De S, Verma S, et al. A brief review of near-
测系统的设计 [J]. 电工技术学报, 2018, 33(10): 2274–2281. est neighbor algorithm for learning and classification[C]//
Liu Suzhen, Rao Nuoxin, Zhang Chuang, et al. Design of International Conference on Intelligent Computing and
electromagnetic ultrasonic nondestructive testing system Control Systems. IEEE, 2019: 1255–1260.
based on LabVIEW[J]. Transactions of China Electrotech- [20] Saini I, Singh D, Khosla A. QRS detection using K-nearest
nical Society, 2018, 33(10): 2274–2281. neighbor algorithm (KNN) and evaluation on standard
[8] Liu M, Wu Y, Song H, et al. Multiparameter measuring ECG databases[J]. Journal of Advanced Research, 2013,
system using fiber optic sensors for hydraulic temperature, 4(4): 331–344.