WANG Hongyao, LI Xiaowei, HAN Yimiao, LYU Xi. Design of damage detection system for mine-used wire rope[J]. Journal of Mine Automation, 2020, 46(6): 92-97. DOI: 10.13272/j.issn.1671-251x.17546
Citation: WANG Hongyao, LI Xiaowei, HAN Yimiao, LYU Xi. Design of damage detection system for mine-used wire rope[J]. Journal of Mine Automation, 2020, 46(6): 92-97. DOI: 10.13272/j.issn.1671-251x.17546

Design of damage detection system for mine-used wire rope

More Information
  • The broken wire on surface of mine-used wire rope would tilt during operation, which may lead to the situation that the wire rope in operation scratches or takes away the inner wall of aperture in flaw detector, and even causes secondary damage to the wire rope, so as to affect detection results. For the above problems, a damage detection system for mine-used wire rope was designed based on ultrasonic ranging principle and strong magnetic detection principle. The system uses ultrasonic ranging device to detect tilting height of broken wire on the surface of wire rope. If the detection value exceeds the limit distance, an alarm would be issued, or else damage of the wire rope would be further detected by strong magnetic detection device. The test results show that in view of the set 20,30,35 mm tilting height of the broken wire, the detection errors of the system are basically not more than ±2 mm, which verify that the system can effectively and accurately detect broken wire on surface of the wire rope as well as its tilting height, so as to avoid the broken wire to damage the strong magnetic detection device caused by too high tilting.
  • Related Articles

    [1]DAI Chaofan, DENG Xiaoxiang, CUI Chengyao, LIU Baotong, SUN Wei. Harmonic detection device based on comb filter and subgroup algorithm[J]. Journal of Mine Automation, 2017, 43(4): 81-85. DOI: 10.13272/j.issn.1671-251x.2017.04.019
    [2]ZHANG Tingzhong, MA Hongyu, SHI Manman, SUN Yidi. Study of harmonics detection based on parametric spectral estimation method[J]. Journal of Mine Automation, 2016, 42(3): 60-64. DOI: 10.13272/j.issn.1671-251x.2016.03.014
    [3]LEI Ruhai, HAO Zhe. A harmonic detection method for power network based on wavelet transform Mallat algorithm[J]. Journal of Mine Automation, 2014, 40(12): 65-69. DOI: 10.13272/j.issn.1671-251x.2014.12.017
    [4]ZHU Gao-zhong. Research of harmonic detection method based on improved wavelet packet transformatio[J]. Journal of Mine Automation, 2013, 39(7): 61-64.
    [5]GONG Mao-fa, ZHANG Xu, ZHU Yi-kai. An improved ip-iq harmonic detection method[J]. Journal of Mine Automation, 2013, 39(1): 70-74.
    [6]ZHOU Xue-feng. Harmonic Detection Method in Power Network Based on FFT Algorithm[J]. Journal of Mine Automation, 2012, 38(3): 38-40.
    [7]ZHANG Ming-guang, HOU Zhi-ju. Research of Power Harmonic Detection Based on Wavelet Packet Transform[J]. Journal of Mine Automation, 2010, 36(6): 67-70.
    [8]LIN Qing-song, CHEN Qing-hua. Research of Problem of Spectrum Leakage in Harmonics Detectio[J]. Journal of Mine Automation, 2010, 36(2): 53-56.
    [9]YU Guang-bin, LI Tai-feng, WANG Yu-feng. Simulation of a Harmonic Detection Algorithm of Power Quality Control Device for Coal Mine and Its Realizatio[J]. Journal of Mine Automation, 2010, 36(1): 21-25.
    [10]WANG Yu-feng, NIU Li, HE Fei. Embedded Harmonic Detection Device Based on Dual-CPUs[J]. Journal of Mine Automation, 2009, 35(7): 48-51.
  • Cited by

    Periodical cited type(9)

    1. 高翼飞,张晓航,畅明,葛帅帅,陈伟. 基于时空图卷积网络的瓦斯体积分数预警效果研究. 中国安全生产科学技术. 2024(01): 58-64 .
    2. 张玲,杨超宇. 基于注意力机制的ResNet-LSTM煤矿瓦斯浓度预测模型. 煤炭技术. 2024(08): 208-213 .
    3. 朱艺轩. 基于机器学习的成都市空气质量预测. 信息记录材料. 2024(07): 160-162 .
    4. 殷建华,戴冠正,丁宁,辛晓钢,张谦,杜荣华. 基于STL-Informer-BiLSTM-XGB模型的供热负荷预测. 科学技术与工程. 2024(21): 8942-8949 .
    5. 胡青松,郑硕,李世银,孙彦景. 基于改进TCN-TimeGAN的矿井瓦斯浓度智能预测方法. 煤炭科学技术. 2024(S2): 321-330 .
    6. 李洪晨,张志强. 全国国民图书阅读率会超过60%吗?——基于ARIMA模型的全国国民阅读调查预测研究. 图书情报工作. 2023(09): 72-80 .
    7. 金秀章,陈佳政,李阳峰. 基于ARIMA-OSELM的火电厂SCR入口NO_x浓度预测建模研究. 计量学报. 2023(09): 1458-1466 .
    8. 曹梅,杨超宇. 基于小波的CNN-LSTM-Attention瓦斯预测模型研究. 中国安全生产科学技术. 2023(09): 69-75 .
    9. 曹梅,杨超宇. 基于双向长短期记忆网络的煤矿瓦斯浓度预测. 绥化学院学报. 2023(12): 156-160 .

    Other cited types(11)

Catalog

    Article Metrics

    Article views (158) PDF downloads (23) Cited by(20)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return