基于电阻抗成像的立木孔洞缺陷检测研究

Research on hole defect detection of standing trees based on electrical impedance imaging

摘要

为实现对树木内部缺陷的识别和无损检测,提出一种基于电阻抗成像的立木孔洞缺陷检测系统设计方法.该系统以STM32F103ZET6为主控板.首先,将正弦激励电压信号输入Howland桥式压控电流源电路,获得输出激励电流;接着,采用16个针式电极,按照相邻激励相邻测量的策略将激励电流循环输入到立木表面电极上并采集电压测量信号;然后,利用数据处理模块对测量的微弱电压信号进行放大、滤波、解调处理,并经由串口传输至上位机;最后,通过图像重建算法重构立木内部生长情况的电导率分布图像,实现立木孔洞缺陷可视化检测.实验结果表明,所提基于电阻抗成像的立木孔洞缺陷检测系统可以有效识别立木孔洞缺陷的位置和轮廓,能够实现立木内部缺陷的无损检测.

Abstract

In order to realize the identification and non-destructive testing of internal defects in trees, a design method of standing tree hole defect detection system based on electrical impedance tomography was proposed. The system uses STM32F103ZET6 as the main control board, firstly, the sinusoidal excitation voltage signal is input into the Howland bridge voltage controlled current source circuit to obtain the output excitation current; Then, 16 needle electrodes were used, and the excitation current was cyclically input to the standing trees surface electrode and the voltage measurement signal was collected according to the strategy of adjacent excitation and adjacent measurement. Then, the data processing module is used to amplify, filter and demodulate the measured weak voltage signal, and transmit it to the upper computer through the serial port. Finally, the image reconstruction algorithm was used to reconstruct the conductivity distribution image of the internal growth of the standing trees, and the visual detection of the hole defects of the standing trees was realized. Experimental results show that the proposed electrical impedance imaging based on standing tree hole defect detection system can effectively identify the location and contour of standing tree hole defects, and can realize the non-destructive detection of standing tree internal defects.

关键词

立木 / 电阻抗成像 / 缺陷检测

Key words

standing tree / electrical impedance tomography / defect detection

参考文献

[1] 杜欣, 王海民. 木材资源开发与保护的双赢创新模式分析[J]. 林产工业, 2020, 57(11): 80-82.

[2] Du X, Wang H M . Analysis on win-win innovation mode of development and protection of wood resources[J]. China Forest Products Industry, 2020, 57(11): 80-82.

[3] 鲁芮伶, 杜莹, 晏黎明, 等 . 森林树木死亡的判定方法及其应用综述[J]. 科学通报, 2019, 64(23): 2395-2409.

[4] Lu R L, Du Y, Yan L M, et al. A methodological review on identification of tree mortality and their applications[J]. Chinese Science Bulletin, 2019, 64(23): 2395-2409.

[5] 徐金梅, 吕建雄, 鲍甫成, 等 . 气候因素对木材细胞结构的影响[J]. 林业科学, 2011, 47(8): 151-158.

[6] Xu J M, Lü J X, Bao F C, et al. Influence of climate factors on wood cell structure[J]. Scientia Silvae Sinicae, 2011, 47(8): 151-158.

[7] 徐华东, 王立海, 宋世全. 原木内部孔洞缺陷二维超声图像构建[J]. 林业科学, 2014, 50(8): 126-130.

[8] Xu H D, Wang L H, Song S Q . Two dimensional image construction of ultrasonic wave for detecting internal hole defect in log[J]. Scientia Silvae Sinicae, 2014, 50(8): 126-130.

[9] 王再超, 李光辉, 冯海林, 等 . 基于应力波和支持向量机的木材缺陷识别分类方法[J]. 南京林业大学学报(自然科学版), 2015, 39(3): 130-136.

[10] Wang Z C, Li G H, Feng H L, et al. A method of wood defect identification and classification based on stress wave and SVM[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2015, 39(3): 130-136.

[11] Schajer G S . Lumber strength grading using X-ray scanning[J]. Forest Products Journal, 2001, 51(1): 43-50.

[12] 孟宪静, 孙天用, 王立海. 基于红外热像技术的木材内部缺陷检测的研究[J]. 森林工程, 2011, 27(6): 33-35.

[13] Meng X J, Sun T Y, Wang L H . Research on internal defect detection of wood based on infrared thermal imaging technology[J]. Forest Engineering, 2011, 27(6): 33-35.

[14] 岳小泉, 王立海, 王兴龙, 等 . 电阻断层成像、应力波及阻抗仪3种无损检测方法对活立木腐朽程度的定量检测[J]. 林业科学, 2017, 53(3): 138-146.

[15] Yue X Q, Wang L H, Wang X L, et al. Quantitative detection of internal decay degree for standing trees based on three NDT methods-electric resistance tomography,stress wave imaging and resistograph techniques[J]. Scientia Silvae Sinicae, 2017, 53(3): 138-146.

[16] 党红云, 许景辉, 刘敏. 基于电阻层析成像的古树病变无损检测方法研究[J]. 林产工业, 2024, 61(8): 26-31.

[17] Dang H Y, Xu J H, Liu M . Research on non-destructive testing method for ancient tree lesions based on electrical resistance tomography imaging[J]. China Forest Products Industry, 2024, 61(8): 26-31.

[18] 左瑞雪, 韩仲鑫. 基于电阻抗成像的立木无损检测系统[J]. 现代电子技术, 2023, 46(24): 61-66.

[19] Zuo R X, Han Z X . EIT-based nondestructive testing system of standing tree[J]. Modern Electronics Technique, 2023, 46(24): 61-66.

[20] 施艳艳, 高振, 王萌, 等 . 基于脑部电阻抗信息的非线性动力学脑卒中诊断方法[J]. 河南师范大学学报(自然科学版), 2023, 51(6): 72-77.

[21] Shi Y Y, Gao Z, Wang M, et al. A nonlinear kinetic method for stroke diagnosis based on cerebral electrical impedance information[J]. Journal of Henan Normal University(Natural Science Edition), 2023, 51(6): 72-77.

[22] 李秀艳, 马会梅, 王琦, 等 . 基于轮廓形状的肺部电阻抗成像三维插值方法[J]. 电子测量与仪器学报, 2021, 35(10): 89-97.

[23] Li X Y, Ma H M, Wang Q, et al. Three dimensional interpolation method of pulmonary electrical impedance tomography based on contour shape[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(10): 89-97.

[24] Zhang Y J, Chen H J, Yang L, et al. A proportional genetic algorithm for image reconstruction of static electrical impedance tomography[J]. IEEE Sensors Journal, 2020, 20(24): 15026-15033.

[25] 张立峰, 佟彤, 肖凯. 基于深度森林及电阻层析成像的气液两相流流型辨识[J]. 计量学报, 2023, 44(6): 893-898.

[26] Zhang L F, Tong T, Xiao K . Flow pattern identification of gas-liquid two-phase flow based on deep forest and electrical resistance tomography[J]. Acta Metrologica Sinica, 2023, 44(6): 893-898.

[27] 王萌, 郑硕, 施艳艳, 等 . 基于单电极激励模式的颅脑电阻抗图像重建方法研究[J]. 河南师范大学学报(自然科学版), 2024, 52(2): 89-95.

[28] Wang M, Zheng S, Shi Y Y, et al. Research on brain electrical impedance tomography method based on single electrode excitation mode[J]. Journal of Henan Normal University(Natural Science Edition), 2024, 52(2): 89-95.

[29] 贡益明, 孔德仁, 商飞. 宽量程高速电阻层析成像系统设计与实验研究[J]. 实验技术与管理, 2024, 41(4): 147-154.

[30] Gong Y M, Kong D R, Shang F . Design and experimental study of electrical resistance tomography system with wide range and high speed[J]. Experimental Technology and Management, 2024, 41(4): 147-154.

[31] 马敏, 余浪, 范文茹. 基于L1/L2正则化电阻抗层析成像算法的碳纤维增强复合材料损伤检测[J]. 激光与光电子学进展, 2023, 60(2): 0212001.

[32] Ma M, Yu L, Fan W R . Detection of carbon-fiber-reinforced polymer damage based on L1/L2 regularization electrical impedance tomography algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0212001.

[33] 王兵贤, 胡康秀, 王泽文. 反问题的Landweber迭代法及其应用研究进展[J]. 计算机应用研究, 2013, 30(9): 2583-2586.

[34] Wang B X, Hu K X, Wang Z W . Advances in research in Landweber iterate method for inverse problems and its application[J]. Application Research of Computers, 2013, 30(9): 2583-2586.

[35] 张维哲, 张承乾, 刘建生, 等 . 基于弹性范数的接地网电阻抗成像算法[J]. 电工技术学报, 2025, 40(3): 676-688.

[36] Zhang W Z, Zhang C Q, Liu J S, et al. Grounding grid electrical impedance tomography algorithm based on elastic norm[J]. Transactions of China Electrotechnical Society, 2025, 40(3): 676-688.

[37] Chen Y, Li K, Han Y . Electrical resistance tomography with conditional generative adversarial networks[J]. Measurement Science and Technology, 2020, 31(5): 055401.

基金资助

国家自然科学基金(52277234)