Research on wear state monitoring of ultrasonic vibration drilling bits based on dual signal fusion
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摘要: 为了监测超声振动钻削过程中钻头的磨损状态, 构建了超声振动钻削钻头的振动信号和AE信号的采集系统, 通过采集不同磨损状态下钻头的振动信号和AE信号, 对其进行小波分解, 得到与钻头磨损相关的特征值, 将二者融合后作为神经网络的输入, 输入至构建的12-10-3的BP神经网络中, 进行钻头磨损状态的识别。试验结果表明, 所建BP神经网络通过振动和AE的融合信号对钻头的有效识别率为91.7%, 可以有效对钻头的磨损状态进行识别。Abstract: In order to monitor the wear status of the drill bit in the process of ultrasonic vibration drilling, an acquisition system for the vibration signal and AE signal of the ultrasonic vibration drilling bit is constructed. By collecting the vibration signal and AE signal of the drill bit under different wear conditions, it is subjected to wavelet decomposition. Obtain the eigenvalues related to the wear of the drill bit, merge the two as the input of the neural network, and input it into the constructed 12-10-3 BP neural network to identify the wear status of the drill bit. The test results show that the built BP neural network has an effective recognition rate of 91.7% for the drill bit through the fusion signal of vibration and AE, which can effectively identify the wear state of the drill bit.
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Key words:
- AE signal /
- wavelet decomposition /
- BP neural network /
- bit wear condition
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表 4 神经网络输出结果
序号 钻头状态 期望输出 实际输出 1 正常磨损 [1 0 0] [0.995 90.016 30.002 1] 2 正常磨损 [1 0 0] [0.987 90.016 10.002 1] 3 正常磨损 [1 0 0] [0.998 20.002 90.018 3] 4 正常磨损 [1 0 0] [0.979 90.019 00.060 7] 5 严重磨损 [0 1 0] [0.002 70.986 40.448 4] 6 严重磨损 [0 1 0] [0.001 90.956 20.019 0] 7 严重磨损 [0 1 0] [0.008 80.995 70.001 3] 8 严重磨损 [0 1 0] [0.023 20.995 60.005 2] 9 崩刃 [0 0 1] [0.005 50.010 30.990 2] 10 崩刃 [0 0 1] [0.004 40.017 50.981 3] 11 崩刃 [0 0 1] [0.064 40.977 30.002 8] 12 崩刃 [0 0 1] [0.017 30.013 30.969 4] 
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