对煤矿矿井提升机钢丝绳损毁的钢丝检测装置的研究(中文2900字,英文2000字)
摘要：为了克服目前国内钢丝故障检测设备的缺陷，如低精度，低灵敏度和不稳定，一个新的由煤矿-提升机钢丝绳所造成的漏磁信号的检测和处理装置已经研制出。强磁场检测的原理应用在该设备中，钢丝由前磁头磁化强度达到饱和。我们特别的特点是安装在沿圆圈方向上传感器的内壁数目通量是在钢丝绳中两倍大的数目。周边组件系列地连接在一起并且由于钢丝的通量域所产生的渗漏对钢丝绳的表面干扰有效地被过滤，，断丝所产生的采样信号序列，其特点是在线缆的表面上由一个三维分布漏磁场通量，可以立体简明和根据特性提取。BP神经网络的模型已经被建立和BP神经网络的算法是用来定量分析地确定有多少钢丝损毁。在我们的研究，我们用了6 × 37 +FC， 24毫米线缆作为我们的测试对象。随机人为地以不同程度破坏和损坏数根钢丝，实验共进行了100次，以为来自我们的样本的100组对象获取数据， 然后将数据输进BP神经网络进行处理。然后该网络用来识别共计16钢丝，打破了5个不同地点。测试数据证明我们的新装置可以提高检测破碎和损坏的钢丝的检测精度。
关键词：钢丝绳;损坏的钢丝;信号处理;检测装置
Research on Detection Device for Broken Wires of Coal Mine-Hoist Cable
WANG Hong-yao1, HUA Gang1, TIAN Jie2
1School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China
2School of Mechanical Electronic and Information Engineering, China University of Mining & Technology, Beijing 100083, China
Abstract: In order to overcome the flaws of present domestic devices for detecting faulty wires such as low precision,low sensitivity and instability, a new instrument for detecting and processing the signal of flux leakage caused by broken wires of coal mine-hoist cables is investigated. The principle of strong magnetic detection was adopted in the equipment. Wires were magnetized by a pre-magnetic head to reach magnetization saturation. Our special feature is that the number of flux-gates installed along the circle direction on the wall of sensors is twice as large as the number of strands in the wire cable. Neighboring components are connected in series and the interference on the surface of the wire cable, produced by leakage from the flux field of the wire strands, is efficiently filtered. The sampled signal sequence produced by broken wires, which is characterized by a three-dimensional distribution of the flux-leakage field on the surface of the wire cable, can be dimensionally condensed and characteristically extracted. A model of a BP neural network is built and the algorithm of the BP neural network is then used to identify the number of broken wires quantitatively. In our research, we used a 6×37+FC, 24 mm wire cable as our test object. Randomly several wires were artificially broken and damaged to different degrees. The experiments were carried out 100 times to obtain data for 100 groups from our samples. The data were then entered into the BP neural network and trained. The network was then used to identify a total 16 wires, broken at five different locations. The test data proves that our new device can enhance the precision in detecting broken and damaged wires.
Key words: wire cable; broken wire; signal processing; detection device |
