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改进抗干扰CNN的变负载滚动轴承损伤程度识别
Damage Degree Identification of Rolling Bearings Under Variable Load with Improved Anti⁃interference CNN
  
DOI:10.16450/j.cnki.issn.1004?6801.2021.04.012
中文关键词:  滚动轴承  损伤程度识别  注意力机制  抗干扰卷积神经网络
英文关键词:rolling bearing  damage degree identification  attention mechanism(AM)  anti-interference convolutional neural network(ACNN)
基金项目:国家自然科学基金资助项目(51775072);重庆市科技创新领军人才支持计划资助项目(CSTCCCXLJRC201920)
作者单位
董绍江1, 裴雪武1, 吴文亮1, 汤宝平2, 赵兴新3 (1.重庆交通大学机电与车辆工程学院 重庆400074) (2.重庆大学机械传动国家重点实验室 重庆400044)(3.重庆长江轴承股份有限公司 重庆401336) 
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中文摘要:
      针对强噪声、变负载工况下滚动轴承损伤程度难以识别的问题,提出了一种基于改进抗干扰卷积神经网络(anti?interference convolutional neural network,简称ACNN)的变负载工况下轴承损伤程度识别新方法。首先,对滚动轴承的一维振动信号进行预处理,得到标签化的数据样本,分为训练集和测试集;其次,将注意力机制引入到卷积神经网络的各个特征提取层中以建立特征提取通道之间的联系,得到基于改进ACNN的变负载工况下轴承损伤程度识别模型;然后,将训练集数据输入到改进ACNN中进行学习,将得到的识别模型应用于测试集,输出损伤程度识别结果,在训练过程中,为了提高模型的抗干扰能力,将Dropout算法引入到卷积层,为抑制过拟合,对原始训练样本进行加噪处理;最后,通过滚动轴承损伤程度模拟试验,在变工况下进行测试。结果表明,在噪声环境中所提方法能更准确地实现变负载工况下的轴承损伤程度识别。
英文摘要:
      Aiming at the problem that it is difficult to identify the damage degree of rolling bearings under strong noise and variable load conditions, a new method based on improved anti-interference convolutional neural network (ACNN) for bearing damage identification under variable load conditions is proposed. First, the one-dimensional vibration signal of the rolling bearing is pre-processed to obtain labeled data samples, which are divided into training set and test set. Then the attention mechanism is introduced into each feature extraction layer of the convolutional neural network to establish the relationship between the feature extraction channels, and an improved model of bearing damage recognition under variable load conditions based on improved ACNN is obtained. After that, the training set data is input into the improved ACNN for learning. The obtained recognition model is applied to the test set, and the results of damage degree recognition are output. During the training process, in order to improve the anti-interference ability of the model, Dropout operation is performed on the convolutional layer. In addition, the original training samples are denoised to suppress overfitting. Finally, through the rolling bearing damage degree simulation test, the test is performed under variable working conditions. The results show that the proposed method can more accurately identify the degree of bearing damage under variable load conditions in noisy environments.
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