BP神经网络在截齿合金头失效识别中的应用
Research on Identification of Pick Alloy Head’s Failure Mode Based on BP Neural Network

为实现采煤机截割过程中截齿合金头失效形式的监测和识别，提出一种基于BP神经网络的多特征信号识别截齿合金头失效形式的方法。测试提取截割过程中合金头龟裂、合金头脱落、合金头崩刃和合金头严重磨损4种截齿x，y，z三个方向上的振动特征信号和截割电机电流特征信号，选取特征值信号的最大值、均值和方差作为特征样本对BP神经网络进行学习和训练，建立截齿合金头失效形式的识别模型，实现截割过程中截齿合金头失效形式在线监测与准确识别。实验结果表明，BP神经网络的判别结果和测试样本的实际失效类型相符，能够对截齿合金头失效形式进行准确识别，为实现采煤机截齿在线监测和失效形式识别提供新的方法和手段。
In order to monitor and identify the failure mode of the alloy head of the pick-cutting, a method based on BP neural network is proposed to deal with the multi-feature signals. The chapped, off, chipping and serious wear alloy head are tested in cutting process to extract the maximum, mean and variance of the three directional vibration characteristic signals and current signal of the cutting motor. The BP neural network is learned and trained by multi-feature signals to establish the recognition model and monitor and identify the failure mode on-line. The experimental results show that the results of BP neural network are consistent with the actual failure mode of the test samples. It provides new methods to realize online monitoring and identification of failure mode for shearer picks.