Page 70 - 《应用声学》2023年第1期
P. 70
66 2023 年 1 月
Xiao Qianghong, Zhou Qiang, Wang Ying, et al. Research 2213.
on detecting method of ceramic structure defect based on [15] 黄沁元, 谢罗峰, 殷国富, 等. 基于变分模态分解和天牛须搜
coin-tap sound time-frequency analysis[J]. China Ceram- 索的磁瓦内部缺陷声振检测 [J]. 振动与冲击, 2020, 39(17):
ics, 2017, 53(9): 47–53. 124–133.
[8] 梁钊. 基于敲击信号的刹车片内部缺陷检测方法研究 [D]. 淄 Huang Qinyuan, Xie Luofeng, Yin Guofu, et al. Acoustic-
博: 山东理工大学, 2019. vibration detection for internal defects of magnetic tile
[9] 蒋志迪. 一种基于小波分解和压缩感知的冲击声学无损检测 based on VMD and BAS[J]. Journal of Vibration and
方法 [J]. 电子与信息学报, 2012, 34(12): 3021–3026. Shock, 2020, 39(17): 124–133.
Jiang Zhidi. A new impact-acoustics non-destructive test
[16] 胡含兵. 基于 MODWPT 与随机森林的模拟电路故障诊断研
method based on wavelet decomposition and compressive
究 [D]. 长沙: 湖南师范大学, 2019.
sensing[J]. Journal of Electronics & Information Technol-
[17] 万晓静, 孙文磊, 陈坤. 小波包能量熵和改进的 LSSVM 在风
ogy, 2012, 34(12): 3021–3026.
力机轴承故障诊断中的应用 [J]. 水电能源科学, 2021, 39(2):
[10] Deng X, Wang Q, Chen H, et al. Eggshell crack detection
142–145.
using a wavelet-based support vector machine[J]. Comput-
Wan Xiaojing, Sun Wenlei, Chen Kun. Application of
ers and Electronics in Agriculture, 2010, 70(1): 135–143.
wavelet packet energy entropy and improved LSSVM in
[11] 陈华华. 风电叶片脱层的无损检测技术研究 [D]. 南京: 南京
fault diagnosis of wind turbine bearings[J]. Water Re-
航空航天大学, 2015.
sources and Power, 2021, 39(2): 142–145.
[12] 张涛, 高新意, 唐伟, 等. 基于神经网络的玻璃缺陷声学检测
[18] 陈石, 张兴敢. 基于小波包能量熵和随机森林的级联 H 桥多
方法 [J]. 声学技术, 2018, 37(5): 488–495.
电平逆变器故障诊断 [J]. 南京大学学报 (自然科学), 2020,
Zhang Tao, Gao Xinyi, Tang Wei, et al. Acoustic detec-
56(2): 284–289.
tion method of glass defects based on neural network[J].
Chen Shi, Zhang Xinggan. Fault diagnosis for cascaded H
Technical Acoustics, 2018, 37(5): 488–495.
[13] 梁钊, 邱晓梅, 王峰, 等. 基于能量特征的刹车片内部缺陷检测 bridge multilevel inverter based on wavelet packet energy
entropy and random forest[J]. Journal of Nanjing Univer-
方法 [J]. 组合机床与自动化加工技术, 2018(11): 89–91. 95.
sity (Natural Science), 2020, 56(2): 284–289.
Liang Zhao, Qiu Xiaomei, Wang Feng, et al. Method of
internal defect detection of brake pads based on energy [19] 杨冬锋, 陈盛开, 刘晓军, 等. 基于自适应 VMD 和时 -频分段
features[J]. Modular Machine Tool & Automatic Manu- 能量熵特征的过电压信号识别 [J]. 电网技术, 2019, 43(12):
facturing Technique, 2018(11): 89–91, 95. 4597–4604.
[14] 冉茂霞, 黄沁元, 刘鑫, 等. 基于优化变分模态分解的磁瓦 Yang Dongfeng, Chen Shengkai, Liu Xiaojun, et al. Re-
内部缺陷检测 [J]. 浙江大学学报 (工学版), 2020, 54(11): search on overvoltage signal recognition based on adap-
2158–2168, 2213. tive VMD and time-frequency segment energy entropy[J].
Ran Maoxia, Huang Qinyuan, Liu Xin, et al. Internal Power System Technology, 2019, 43(12): 4597–4604.
defect detection of arc magnets based on optimized vari- [20] Mahmud K, Azam S, Karim A, et al. Machine learning
ational mode decomposition[J]. Journal of Zhejiang Uni- based PV power generation forecasting in Alice springs[J].
versity (Engineering Science), 2020, 54(11): 2158–2168, IEEE Access, 2021, 9: 46117–46128.