摘要
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 bro-ken 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 se-quence 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 neu-ral 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,Φ24 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.
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, φ24 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.
