首页|基于多域信息融合与深度分离卷积的轴承故障诊断网络模型

基于多域信息融合与深度分离卷积的轴承故障诊断网络模型

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针对传统卷积神经网络(CNN)对滚动轴承振动信号的故障识别准确率不高这一问题,提出了一种基于多域信息融合结合深度分离卷积(MDIDSC)的轴承故障诊断方法.首先,利用 自适应噪声的完全集合经验模态分解(CEEMDAN)算法对轴承振动信号进行了分解;然后,利用分解出的本征模态函数(IMF)的各个分量构建了多空间状态矩阵,并将该多空间状态矩阵输入该深度分离卷积模型中,进行了卷积训练;同时,在该深度分离卷积模型中添加了残差结构,对数据特征进行了复利用,并对卷积核进行了深度分离,解决了深度模型的网络退化问题;最后,提出了一种空间特征提取方法,对模型参数进行了修剪,采用一种自适应学习率退火方法进行了梯度优化,以避免模型陷入局部最优.研究结果表明:通过对多个轴承故障数据集进行对比分析可知,MDIDSC在轴承故障诊断方面的准确率和稳定性明显优于其他方法,MDIDSC的最高测试准确率为100％,平均测试准确率为99.07％;同时,在测试集中的最大损失和平均损失分别为0.134 5和0.084 1;该结果表明MDIDSC在轴承故障诊断方面具有一定的优越性.
Bearing fault diagnosis network modal based on multi-domain information fusion and depth separation convolutions
In response to the problem of low accuracy in fault diagnosis of rolling bearing vibration signals using traditional convolutional neural network(CNN),a superior bearing fault diagnosis method that combined multi domain information with deep separation convolution(MDIDSC)was proposed.Firstly,the bearing vibration signal was decomposed using the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm.Then,a multi-spatial state matrix was constructed using the decomposed intrinsic mode functions(IMF)components,and the multi-spatial state matrix was inputted into the proposed deep separation convolutional model for training the proposed network.At the same time,residual structures were added to the deep separation convolutional model to reuse the feature extracted by the proposed method,and the convolutional kernel was separated in the direction of depth,solving the problem of network degradation in the deep model.Finally,a spatial feature extraction method was proposed to prune the model parameters for improving the efficiency of the proposed method,and an adaptive learning rate annealing method was used to avoid the model falling into local optimization in the process of the gradient optimizing.The experimental results indicate that,through a lot of experimental comparisons between different bearing fault datasets,the proposed method exhibits more excellent performance and outstanding ability in recognizing the bearing fault with a maximum testing accuracy of 100％,and the mean accuracy of 99.07％.At the same time,the maximum and mean loss of the proposed method are respectively 0.134 5 and 0.084 1 in test dataset,which demonstrates the superiority of the proposed method in diagnosing bearing fault.

depth separation convolutionsinformation fusionparameter pruningresidual networkconvolutional neural network(CNN)complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)intrinsic mode functions(IMF)multi domain information with de

王同、许昕、潘宏侠

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中北大学机械工程学院,山西太原 030051

中北大学系统辨识与诊断技术研究所,山西太原 030051

深度分离卷积 信息融合 参数修剪 残差网络 卷积神经网络 自适应噪声的完全集合经验模态分解 本征模态函数 多域信息融合结合深度分离卷积

内燃机可靠性国家重点实验室基金资助项目

skler-201911

2024

10.3969/j.issn.1001-4551.2024.01.003

机电工程
浙江大学　浙江省机电集团有限公司

机电工程

CSTPCD北大核心
影响因子：0.785
ISSN：1001-4551
年,卷(期)：2024.41(1)
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