摘要
针对现有的、基于深度卷积神经网络的故障诊断方法利用池化层对高阶输入张量降维时容易破坏张量数据,造成数据信息丢失,以及网络结构相对复杂的不足,构造了一种深度张量投影网络。该网络利用张量投影层代替传统卷积神经网络中的池化层,在对输入的高阶张量数据进行降维时,不会对张量数据造成破坏,避免了特征信息的丢失,提高了模型对故障的识别准确率;并且张量投影层是一种维度可变的降维层,可以简化网络结构。在此基础上,结合高阶谱和深度张量投影网络各自的优点,提出了基于深度张量投影网络的机械故障诊断方法。在提出的方法中,利用高阶谱提取故障信号特征,将得到的高阶张量谱图输入到构建的深度张量投影网络模型中进行高阶张量降维和识别。提出的方法成功应用到齿轮箱故障诊断中。实验结果表明,所提方法能够更好地保留原始故障信息,有效识别不同类型的故障,准确率优于传统深度卷积神经网络故障诊断方法。
The shortcomings of fault diagnosis methods based on deep convolutional neural networks is that,tensor data is easily destroyed when reducing the dimension of high-order input tensors by pooling layers,which results in a loss of data information,and the relatively complex network structure.Therefore,a Deep TensorProjection Networks method is constructed via replacing the pooling layer in the traditional CNN by a TensorProjection Layer.The TensorProjection Layer reduces the dimensionality of input high-order tensor data without causing damage to the data,thus avoiding the impact of the loss of feature information,and greatly improving the recognition accuracy of the model.The dimensionality of the TensorProjection Layer used for dimensionality reduction is variable,thus simplifying the networks structures.Based on this,combined with the respective advantages of high-order spectrum and deep TensorProjection networks,a mechanical fault diagnosis method based on deep TensorProjection networks is proposed.In the proposed method,the feature of fault signal is extracted by high-order tensor spectrum,which is input into the constructed model for reducing high-order tensor dimensionality and identifying faults.The proposed method is applied to diagnose gearbox faults.Experimental results show that the proposed method can better retain the original fault information and effectively recognize the different types of faults.And the accuracy is better than traditional deep convolutional neural network fault diagnosis methods.
作者
黄文静
李志农
王发麟
陈亮亮
龙盛蓉
HUANG Wen-jing;LI Zhi-nong;WANG Fa-lin;CHEN Liang-liang;LONG Sheng-rong(Key Laboratory of Nondestructive Testing of Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2024年第4期657-666,共10页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(52075236)
江西省自然科学基金重点项目资助项目(20212ACB202005)。
关键词
故障诊断
深度张量投影网络
高阶谱
fault diagnosis
Deep TensorProjection Networks
high-order spectrum
作者简介
黄文静(1998-),女,硕士研究生。E-mail:1341065370@qq.com;通讯作者:李志农(1966-),男,教授,博士生导师。E-mail:lizhinong@tsinghua.org.cn。
