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 caus...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.展开更多
现有的信号识别方法马修斯相关系数较低、识别效果差,因此研究基于小波分析的绝缘架空导线局部放电信号识别方法。运用安全光栅传感器(Sensor to Logic Converter,SLC)设备采集数据,提取局部放电类型的脉冲特征参数,利用小波变换对绝缘...现有的信号识别方法马修斯相关系数较低、识别效果差,因此研究基于小波分析的绝缘架空导线局部放电信号识别方法。运用安全光栅传感器(Sensor to Logic Converter,SLC)设备采集数据,提取局部放电类型的脉冲特征参数,利用小波变换对绝缘架空导线局部放电信号故障数据进行去噪。在反向传播(Back Propagation,BP)网络中对得到的数据进行预处理,根据误差反向传播对BP网络进行训练,提取不同种类的局部放电信号特征量完成识别。实验结果表明,所提方法的马修斯相关系数较高,可达到73%,能够对绝缘架空导线局部放电信号进行有效识别。展开更多
文摘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.
文摘现有的信号识别方法马修斯相关系数较低、识别效果差,因此研究基于小波分析的绝缘架空导线局部放电信号识别方法。运用安全光栅传感器(Sensor to Logic Converter,SLC)设备采集数据,提取局部放电类型的脉冲特征参数,利用小波变换对绝缘架空导线局部放电信号故障数据进行去噪。在反向传播(Back Propagation,BP)网络中对得到的数据进行预处理,根据误差反向传播对BP网络进行训练,提取不同种类的局部放电信号特征量完成识别。实验结果表明,所提方法的马修斯相关系数较高,可达到73%,能够对绝缘架空导线局部放电信号进行有效识别。
