Application of Improved Deep Belief Network in Electric Spindle Fault Diagnosis
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摘要: 针对加工中心电主轴中滚动轴承等零部件容易出现故障或者失效等问题, 即提出一种改进的DBN(深度置信网络)电主轴故障诊断方法。该方法对电主轴中滚动轴承运行故障状态下的振动信号进行特征提取, 然后通过DBN映射出信号与故障特征的复杂关系来进行故障诊断。其中为提高训练DBN的效率以及解决在反向传播过程中梯度消失的问题, 提出一种新型激活函数。研究结果表明, 采用新型激活函数的DBN不仅降低了时间成本, 同时也具有较高的故障识别的能力。Abstract: Aiming at the problems such as component failure and rolling bearing fault in the electric spindle of machining centers, an improved DBN(Deep belief network) electric spindle fault diagnosis method is proposed in this paper. The method extracts features from vibration signals of rolling bearings in the electric spindle under running fault conditions, and then maps the complex relationship between the signal and the fault feature through the DBN to perform fault diagnosis. In order to improve the efficiency of training DBN and solve the problem of gradient disappearance during backpropagation, a new activation function is established. The research results show that the DBN with the new activation function not only reduces the time cost, but also has the higher ability of fault identification.
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表 1 轴承故障数据集类型描述
故障类型 故障深度/Inch 数据集A1 数据集A2 数据集A3 数据集A0 故障类别 正常 0 50 50 50 150 1 内1 0.007 50 50 50 150 2 内2 0.014 50 50 50 150 3 内3 0.021 50 50 50 150 4 外1 0.007 50 50 50 150 5 外2 0.014 50 50 50 150 6 外3 0.021 50 50 50 150 7 滚1 0.007 50 50 50 150 8 滚2 0.014 50 50 50 150 9 滚3 0.021 50 50 50 150 10 
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表 2 不同激活函数的故障识别正确率
激活函数 Sigmoid Tanh IM-Tanh Relu 正确率/% 91.2 89.5 99.7 86.6 迭代数 830 850 630 920 时间t/s 41.254 4 35.583 1 30.877 6 33.176 5
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