Damage identification of the mining wire rope based on adaptive moving average denoise and BP neural network
摘 要
提出了一种基于自适应移位平均降噪与BP神经网络的钢丝绳损伤识别方法,解决了强噪声背景下的钢丝绳损伤识别问题。以矿井钢丝绳为检测对象,采用自适应移位平均法对含噪的断丝信号与磨损信号进行降噪处理,通过自适应粒子群优化(APSO)算法找到移位平均算法的最优窗宽;然后,以断丝损伤为例,对输出的最优降噪信号提取峰峰值、波宽、波形下面积三种特征值作为特征值样本,将样本归一化后输入BP神经网络进行损伤识别;最后,通过试验验证了所提方法的有效性。试验结果表明,该方法能定性识别钢丝绳损伤并且识别准确率高。
Abstract
A wire rope damage identification method based on adaptive displacement average noise reduction and BP neural network was proposed to solve the problem of wire rope damage recognition under the background of strong noise.Taking the mining wire rope as an example, firstly, the adaptive moving average method was used to denoise the broken wire signal and wear signal, and the optimal window width of the moving average algorithm was found through the adaptive particle swarm optimization (APSO); Then, taking the broken wire damage as an example, three eigenvalues of the output optimal noise reduction signal were extracted as eigenvalue samples: peak to peak value, wave width and area under waveform. The samples were normalized and put into BP neural network for damage identification; Finally, the effectiveness of the proposed method was verified by experiments. The experimental results show that this method can qualitatively identify the wire rope damage, and the recognition accuracy is high.
中图分类号 TG115.28 DOI 10.11973/wsjc202208003
所属栏目 试验研究
基金项目
收稿日期 2021/12/22
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备注吴东(1982-),男,高级工程师,主要研究方向为矿山提升系统与安全管理
引用该论文: WU Dong,ZHANG Baojin,SUI Xianjun,LIU Weixin,HUANG Shengping,YANG Jianhua. Damage identification of the mining wire rope based on adaptive moving average denoise and BP neural network[J]. Nondestructive Testing, 2022, 44(8): 14~19
吴东,张宝金,隋显俊,刘伟新,黄升平,杨建华. 基于自适应移位平均降噪与BP神经网络的钢丝绳损伤识别[J]. 无损检测, 2022, 44(8): 14~19
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参考文献
【1】李玉瑾.矿井提升系统的装备技术与展望[J].煤炭工程,2014,46(10):61-64.
【2】杨叔子,康宜华,陈厚桂.钢丝绳电磁无损检测[M].北京:机械工业出版社,2017.
【3】隋宝峰,王衍吉,陈勇,等.基于BKT检测仪的摩擦提升机钢丝绳无损检测及应用[J].黄金,2017,38(12):39-42.
【4】奚彩萍,张淑宁,熊刚,等.多重分形降趋波动分析法和移动平均法的分形谱算法对比分析[J].物理学报,2015,64(13):136403.
【5】王龙,潘存治,王彦,等.钢丝绳缺陷漏磁信号的降噪及波形特征的提取[J].矿山机械,2014,42(2):49-53.
【6】窦连城,战卫侠,白晓瑞.钢丝绳内外部断丝损伤识别[J].工矿自动化,2021,47(03):83-88.
【7】李丹丹.钢丝绳断丝损伤识别方法研究[D].徐州:中国矿业大学,2019.
【8】钟小勇,刘志辉.基于IPSO-BP神经网络的钢丝绳断丝损伤识别模型研究[J].中国安全生产科学技术,2020,16(4):70-75.
【9】张英男.改进的粒子群优化算法(APSO和DPSO)研究[D].大连:大连理工大学,2008.
【10】张楠.基于小波变换的钢丝绳断丝定量检测技术研究[J].煤炭工程,2013,45(2):129-131.
【11】王龙,潘存治,王彦,等.钢丝绳缺陷漏磁信号的降噪及波形特征的提取[J].矿山机械,2014,42(2):49-53.
【12】田志勇,张耀,谭继文.基于BP神经网络的钢丝绳断丝定量检测[J].煤炭学报,2006,31(2):245-249.
【2】杨叔子,康宜华,陈厚桂.钢丝绳电磁无损检测[M].北京:机械工业出版社,2017.
【3】隋宝峰,王衍吉,陈勇,等.基于BKT检测仪的摩擦提升机钢丝绳无损检测及应用[J].黄金,2017,38(12):39-42.
【4】奚彩萍,张淑宁,熊刚,等.多重分形降趋波动分析法和移动平均法的分形谱算法对比分析[J].物理学报,2015,64(13):136403.
【5】王龙,潘存治,王彦,等.钢丝绳缺陷漏磁信号的降噪及波形特征的提取[J].矿山机械,2014,42(2):49-53.
【6】窦连城,战卫侠,白晓瑞.钢丝绳内外部断丝损伤识别[J].工矿自动化,2021,47(03):83-88.
【7】李丹丹.钢丝绳断丝损伤识别方法研究[D].徐州:中国矿业大学,2019.
【8】钟小勇,刘志辉.基于IPSO-BP神经网络的钢丝绳断丝损伤识别模型研究[J].中国安全生产科学技术,2020,16(4):70-75.
【9】张英男.改进的粒子群优化算法(APSO和DPSO)研究[D].大连:大连理工大学,2008.
【10】张楠.基于小波变换的钢丝绳断丝定量检测技术研究[J].煤炭工程,2013,45(2):129-131.
【11】王龙,潘存治,王彦,等.钢丝绳缺陷漏磁信号的降噪及波形特征的提取[J].矿山机械,2014,42(2):49-53.
【12】田志勇,张耀,谭继文.基于BP神经网络的钢丝绳断丝定量检测[J].煤炭学报,2006,31(2):245-249.
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