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高强螺栓应力锈蚀的声发射特征

霍林生, 田树晓, 王靖凯, 罗明璋

霍林生, 田树晓, 王靖凯, 罗明璋. 高强螺栓应力锈蚀的声发射特征[J]. 无损检测, 2022, 44(1): 42-48. DOI: 10.11973/wsjc202201010
引用本文: 霍林生, 田树晓, 王靖凯, 罗明璋. 高强螺栓应力锈蚀的声发射特征[J]. 无损检测, 2022, 44(1): 42-48. DOI: 10.11973/wsjc202201010
HUO Linsheng, TIAN Shuxiao, WANG Jingkai, LUO Mingzhang. Acoustic emission characteristics of stress corrosion of high strength bolts[J]. Nondestructive Testing, 2022, 44(1): 42-48. DOI: 10.11973/wsjc202201010
Citation: HUO Linsheng, TIAN Shuxiao, WANG Jingkai, LUO Mingzhang. Acoustic emission characteristics of stress corrosion of high strength bolts[J]. Nondestructive Testing, 2022, 44(1): 42-48. DOI: 10.11973/wsjc202201010

高强螺栓应力锈蚀的声发射特征

基金项目: 

中央高校基本科研业务费资助项目(DUT19TD26)

详细信息
    作者简介:

    霍林生(1975-),男,教授,主要研究方向为结构健康监测

    通讯作者:

    霍林生, E-mail:lshuo@dlut.edu.cn

  • 中图分类号: TB553;TG115.28

Acoustic emission characteristics of stress corrosion of high strength bolts

  • 摘要: 高强螺栓是钢结构的重要连接构件,其在高应力服役过程中易因锈蚀而出现断裂。为了研究其在应力锈蚀下的损伤演化规律,利用声发射技术对其损伤过程进行了实时监测。结合改进的K均值算法和动态门槛,对声发射损伤信号进行聚类,并进一步对聚类的声发射信号进行锈蚀损伤阶段划分。试验结果表明,高强螺栓的损伤信号可以分为3类,进一步划分其中第三类信号的损伤阶段,最终得到应力锈蚀的4个阶段,验证了提出的声发射方法的有效性,并可使用该声发射方法实时监测高强螺栓的健康状况。
    Abstract: High strength bolts are important connecting components of steel structures, and they are prone to fracture due to corrosion during high stress service. In order to study its damage evolution law under stress corrosion, real-time monitoring of its damage process was carried out using acoustic emission technology. Combining the improved K-means algorithm and the dynamic threshold, the acoustic emission damage signals were clustered, and the clustered acoustic emission signals are further divided into rust damage stages. The test results show that the damage signals of high strength bolts can be divided into three categories, and the damage stage of the third category of signals is further divided, and finally four stages of stress corrosion are obtained. The test verified the effectiveness of the proposed acoustic emission method, and the acoustic emission method can be used to monitor the health of high-strength bolts in real time.
  • [1] 张洪, 刘彬彬.应用深度学习识别法兰螺栓连接状态[J].应用声学, 2021, 40(3):350-357.
    [2]

    NEWMAN R C, PROCTER R P M.Stress corrosion cracking:1965-1990[J].British Corrosion Journal, 1990, 25(4):259-270.

    [3]

    SIERADZKI K, NEWMAN R C.Stress-corrosion cracking[J].Journal of Physics and Chemistry of Solids, 1987, 48(11):1101-1113.

    [4]

    WANG J K, HUO L S, LIU C G, et al.Feasibility study of real-time monitoring of pin connection wear using acoustic emission[J].Applied Sciences, 2018, 8(10):1775.

    [5]

    WANG J K, HUO L S, LIU C G, et al.Wear degree quantification of pin connections using parameter-based analyses of acoustic emissions[J].Sensors, 2018, 18(10):3503.

    [6] 黄华斌, 彭智伟, 王竹林, 等.飞机铆接壁板疲劳损伤的声发射检测[J].无损检测, 2020, 42(12):12-14, 75.
    [7] 王亚飞, 柴文革, 宋义敏.双差定位法在岩石声发射中的应用[J].无损检测, 2020, 42(8):25-29.
    [8] 汪家送, 顾建祖, 骆英, 等.螺旋肋钢丝与混凝土粘结滑移的声发射检测[J].无损检测, 2009, 31(10):806-809.
    [9] 张磊, 何建军, 程庆阳, 等.风机叶片连接螺栓损伤的在线监测[J].无损检测, 2021, 43(5):64-68.
    [10] 刘卫东, 丁恩杰, 童敏明.煤矿声发射监测中传感器阵列的布置[J].无损检测, 2010, 32(5):338-341, 356.
    [11] 王文友, 吴克勤, 张瑞琳, 等.飞机关键零部件疲劳损伤的声发射实时监测[J].无损检测, 2009, 31(6):481-484.
    [12]

    URBAHS A, CARJOVA K, FESCUKS J.Analysis of the results of acoustic emission diagnostics of a structure during helicopter fatigue tests[J].Aviation, 2017, 21(2):64-69.

    [13]

    LEAMAN F, CLAUSEN E, BALTES R, et al.Analysis of acoustic emission signals during fatigue testing of a M36 bolt using the Hilbert-Huang spectrum[J].Structural Monitoring and Maintenance, 2020, 7(1):19-31.

    [14]

    CHEN S W, YANG C H, WANG G B, et al.Similarity assessment of acoustic emission signals and its application in source localization[J].Ultrasonics, 2017, 75:36-45.

    [15] 付玉.}T300碳纤维复合材料损伤声发射特性研究[D].大庆:东北石油大学, 2015.
    [16] 蒋鹏, 张璐莹, 李英年, 等.基于K均值聚类的海洋平台T型管节点损伤声发射信号的模式识别[J].无损检测, 2015, 37(8):48-50, 56.
    [17] 初嘉鹏, 贺凤宝. 机械设计基础[M].北京:中国计量出版社, 2006.
    [18]

    DAVIES D L, BOULDIN D W.A cluster separation measure[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979, 1(2):224-227.

    [19] 王平光.}桥梁拉索腐蚀损伤声发射监测及模式识别[D].大连:大连理工大学, 2015.
    [20] 宫羽丽.}金属腐蚀声发射的特征研究[D].大庆:东北石油大学, 2014.
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出版历程
  • 收稿日期:  2021-07-05
  • 刊出日期:  2022-01-09

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