矿用钢丝绳断丝特征的识别  

Identification of Characteristics for Mine- used Steel Rope Broken Wires

作　　者：李兆星[1] 乔铁柱[1] 

机构地区：[1]太原理工大学新型传感器与智能控制教育部山西省重点实验室,山西太原030024

出　　处：《煤矿安全》2016年第2期123-126,共4页Safety in Coal Mines

基　　金：山西省特色学科资助项目[晋教财[2012]145号](80010302010053)

摘　　要：金属磁记忆检测技术能够检测矿用钢丝绳的断丝也能评估其应力集中部位,但是钢丝绳检测一般需要实时在线检测,检测的磁记忆信号混有许多噪声,为了能够提高钢丝绳断丝识别率,需要对检测信号进行降噪处理。采用集合经验模态分解(EEMD)对检测信号降噪结果看出,此方法明显比小波降噪方法好很多,提出了一种筛选本征模态函数(IMF)的方法。Metal magnetic memory testing technology can test the broken wires and evaluate the stress concentration area of mining steel rope. However, the test of rope always needs real-time online detecting; the tested signal contains a lot of noise. To improve the recognition rate of broken wires, the signal needs to be denoised. The denoising result shows that using Ensemble Empirical Mode Decomposition （EEMD） to denoise the signal is better than wavelet denoising. A new method is proposed to filtrate Intrinsic Mode Functions （IMF）.

关 键 词：钢丝绳断丝识别 集合经验模态分解 金属磁记忆检测 信噪比 矿用钢丝绳 在线检测 

分 类 号：TD532[矿业工程—矿山机电]