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国际刊号:1004-6801
国内刊号:32-1361/V
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  • 主管:中华人民共和国工业和信息化部
  • 主办:南京航空航天大学
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  • 国内刊号:32-1361/V
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基于LSTM-RNN的滚动轴承故障多标签分类方法
A Multi-label Fault Classification Method for Rolling Bearing Based on LSTM-RNN
  
DOI:10.16450/j.cnki.issn.1004-6801.2020.03.020
中文关键词:  滚动轴承  故障分类  多标签分类  循环神经网络  长短时记忆
英文关键词:rolling bearing  fault classification  multi-label classification  recurrent neural network (RNN)  long short-term memory (LSTM)
基金项目:国家自然科学基金资助项目((51575497;U1809219)
作者单位
池永为1,杨世锡1,焦卫东2 (1.浙江大学机械工程学院杭州310027)(2.浙江师范大学工学院金华321004) 
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中文摘要:
      为了提高长短时记忆神经网络模型(long short-term memory recurrent neural network, 简称LSTM-RNN)对滚动轴承故障分类的正确率并减少训练样本量,提出一种基于多标签LSTM-RNN的滚动轴承故障分类方法。首先,建立滚动轴承故障信号仿真模型,分析滚动轴承故障仿真信号频谱特征及其故障分类特点;其次,结合多标签LSTM-RNN模型结构特点,对滚动轴承频谱特征向量进行编码,并利用仿真故障信号验证多标签LSTM-RNN分类方法的有效性;最后,搭建滚动轴承故障模拟试验台,采集3类转速不同故障类型滚动轴承故障振动信号,并采用3种特征提取方法得到共9组试验数据,基于该数据对多标签LSTM-RNN分类方法和单标签LSTM-RNN分类方法进行对比试验。试验结果表明:多标签LSTM-RNN分类方法相比于单标签LSTM-RNN分类方法,平均分类正确率从69.07%提高到99.21%;在保证两种分类方法正确率相近情况下,多标签LSTM-RNN分类方法训练所需样本量比单标签LSTM-RNN分类方法平均减少69.55%。多标签LSTM-RNN分类方法适用于复杂振动信号分类,对于实现快速准确的旋转机械故障诊断具有应用价值。
英文摘要:
      The fault classification method based on long short-term memory recurrent neural network (LSTM-RNN) model is improved based on multiple labels in light of higher accuracy and less training samples when classify rolling bearing faults based on the traditional algorithm. First, a simulation model of rolling bearing fault signal is established, and the spectrum and classification of the rolling bearing fault simulation signal are analyzed. Second, the spectrum feature vectors of the rolling bearing are coded based on the structural characteristics of the multi-label LSTM-RNN model. The effectiveness of a multi-label classification method based on LSTM-RNN is verified by the simulated fault signal. Finally, a test platform is established to collect rolling bearing faults at three different speeds. The multi-label LSTM-RNN classification method and single-label method are compared based on nine groups of data extracted by three methods. The experimental results show that the average classification accuracy of multi-label LSTM-RNN classification increases to 99.21% from 69.07% of the single-label method. The sample size reduces by 69.55% compared with that of the single-label method when the correctness rates of the two classification methods are similar. The multi-label classification method based on LSTM-RNN is suitable for complex vibration signals, which is practically useful in realizing fast and accurate fault diagnosis of rotating machinery.
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