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林业科学 ›› 2019, Vol. 55 ›› Issue (6): 96-102.doi: 10.11707/j.1001-7488.20190612

• 论文与研究报告 • 上一篇    下一篇

不同含水率云南松声发射信号特征

李杨, 许飞云   

  1. 东南大学机械工程学院 南京 211189
  • 收稿日期:2017-05-27 修回日期:2017-07-08 出版日期:2019-06-25 发布日期:2019-07-11
  • 基金资助:
    国家自然科学基金项目"基于近场动力学的大型起重机机械结构损伤机理及其识别方法研究"(51575101)。

Acoustic Emission Signal Characteristics of Pinus yunnanensis with Different Moisture Content

Li Yang, Xu Feiyun   

  1. School of Mechanical Engineering, Southeast University Nanjing 211189
  • Received:2017-05-27 Revised:2017-07-08 Online:2019-06-25 Published:2019-07-11

摘要: [目的]研究不同含水率条件下云南松试件声发射信号的传播规律,探讨含水率对声发射信号波形的响应,为云南松声发射源定位提供依据,为木材内部缺陷无损检测提供基础数据。[方法]以云南松为试验材料,采用NI高速数据采集设备和LabVIEW软件构建云南松试件声发射信号采集平台,利用铅芯折断模拟声发射源,对绝干、气干、生材和饱水4种含水率状态下的试件表面进行声发射信号采集,通过时差法计算信号在4种含水率状态下的平均传播速率,并运用小波分析对声发射信号波形进行分解和重构,根据软阈值量化方法消除各通道系数和经阈值量化后的各高频层系数,去除非主能量信号以便从噪声中析取微弱的声发射信号。[结果]试验中传感器均以接收表面波信号为主;随着含水率增加,云南松试件表面声发射信号波形和平均声速均大幅度衰减,绝干状态下声发射信号时域波形幅值达到±5.2 V,平均声速可达4 208.77 m·s-1,而饱水状态下信号时域波形幅值仅为±0.6 V,平均声速降至1 414.07 m·s-1,气干和生材状态下信号时域波形幅值和平均声速分别为±4、±2 V和3 331.79、2 328.73 m·s-1,且各含水率状态之间平均声速差在876.98~1 003.06 m·s-1范围内;小波变换能有效将"淹没"在噪声中的声发射信号析取出来,4种含水率状态下试件声发射信号频域波形范围在40~150 kHz之间,且气干状态下波形峰值出现在110 kHz左右,其余3种均在50 kHz左右达到峰值。[结论]含水率增加可显著改变云南松声发射信号和传播特征,其信号波形和平均声速均与含水率降低呈正比;小波变换在信号降噪处理方面具有明显优势,不仅可去除信号中的大量噪音,而且不会破坏有用信号,保证信号完整性,能更大程度降低对不同含水率云南松试件声发射信号的分析误差,为云南松表面声发射源定位研究及内部无损检测给予试验数据支持;作为一种木材声发射信号采集与分析平台,研究结果可为不同含水率云南松受压变形破坏全过程的声发射特征分析提供必要的基础理论依据。

关键词: 云南松, 含水率, 平均声速, 声发射特性, 小波分析

Abstract: [Objective] In this study, Pinus yunnanensis is used as the material to analyze the propagation rule of the acoustic emission(AE)signal in samples under different moisture content conditions, and to discuss the response of moisture content to AE signal waveform, which could provide the basis for the location of AE source of P. yunnanensis and the basic data for nondestructive testing of wood internal defects.[Method] The most common P. yunnanensis in Yunnan Province, is used as raw material, containing four kinds of water-bearing states,those were, absolute dry, air-dried, green timber and water-saturated. According to the NI high speed data acquisition equipment and the LabVIEW software, the wood AE signal acquisition platform is set up. Then, the AE signal is collected on the surface of four kinds of wood samples by the simulation of AE source under lead core fracture. Meanwhile, the time difference method is used to calculate the average velocity of four kinds of water condition, and wavelet analysis is used to decompose and reconstruct the AE signal waveform, then soft threshold quantization method is applied for removing each channel coefficient and the high frequency coefficients quantized, and removing non-primary energy signals in order to extract the weak acoustic emission signal from the noise.[Result] In the experiment, the surface wave signals were mainly received by the sensors. With the increase of moisture content, the AE signal waveform and average sound speed of P. yunnanensis are greatly attenuated on the surface. Under absolute dry state, the time domain waveform of AE signal reaches 5.2 V, and the average sound speed can reach 4 208.77 m·s-1, while the amplitude of the signal waveform is only 0.6 V, and the average sound speed is decreased to 1 414.07 m·s-1 in water-saturated state. The amplitude and average rate of signal waveforms are ±4 V, ±2 V and 2 328.73 m·s-1, 3 331.79 m·s-1, respectively, in air-dried and green timber states, and the average sound velocity difference between each water bearing state is in the range of 876.98-1 003.06 m·s-1. Moreover, the AE signals submerged in noise can be extracted by the method of wavelet analysis. Thus, the AE signal of four kinds of samples is obtained, and the range of frequency waveform is between 40 and 150 kHz, while the peak value of waveform appears at about 110 kHz in the air dry state, and the other three are peaked at about 50 kHz.[Conclusion] The increase of moisture content significantly changes the AE signal and propagation characteristics of P. yunnanensis, and its signal waveform and average sound speed are positively proportional to the decrease of moisture content. From the comparison of time-frequency diagram of signal before and after wavelet transform, it can be seen that the wavelet transform has obvious advantage in signal noise reduction processing, not only a lot of noise in the signal was removed, but also the useful signal was not damaged, moreover, the signal integrity was guaranteed. On the other hand, to a greater extent, the analysis error is reduced, which gives experimental data support for the research of P. yunnanensis AE source location and internal non-destructive testing. As an AE signal of acquisition and analysis platform, the result of this study could provide the necessary theoretical evidence for the research of AE characteristics of P. yunnanensis with different moisture contents in process of compression deformation and failure.

Key words: Pinus yunnanensis, moisture contents, average sound velocity, acoustic emission characteristics, wavelet analysis

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