One-dimensional Multi-scale Convolution Neural Network and its Application in Rolling Bearing Fault Diagnosis
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摘要: 为了有效利用来自实际生产中监测系统的海量数据, 并结合一维卷积网络在处理一维数据的优势, 提出一种端到端的一维多尺度卷积神经网络滚动轴承故障诊断方法。首先使用两个一维卷积层和池化层将输入振动信号的长度缩减并增加通道数, 然后利用多尺度并行一维卷积核对上层输出特征进行不同尺度上的反复提取和重构, 最后将提取到的特征输入到一个全连接层进行故障分类。为验证算法的有效性, 通过对滚动轴承不同工况、不同训练样本以及与支持向量机、BP神经网络和循环神经网络等算法对比分析。结果表明提出的模型及方法具有较好的识别效果, 滚动轴承故障诊断正确率达到99.78%。Abstract: In order to use the massive data from the monitoring system in actual production effectively, and combine the advantages of one-dimensional convolution network in processing one-dimensional data, a new rolling bearing fault diagnosis method based on end-to-end one-dimension multi-scale convolution neural network is proposed. Firstly, two one-dimensional convolution layers and pooling layers are used to reduce the length of the input vibration signal and increase the number of channels. Then, multi-scale parallel one-dimensional convolution check is used to extract and reconstruct the output features on different scales repeatedly. Finally, the extracted features are input to a full connection layer for fault classification. In order to verify the effectiveness of the method, by comparing and analyzing the different working conditions, different training samples of rolling bearing and other algorithms such as support vector machine, BP neural network and cyclic neural network, the simulation results show that the proposed model and method has better recognition effect, and the accuracy of rolling bearing fault diagnosis reaches 99.78%.
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Key words:
- rolling bearing /
- fault diagnosis /
- one-dimensional convolution network /
- deep learning
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表 1 各分支中卷积核个数
分支 1×1卷积核个数 3×1卷积核个数 5×1卷积核个数 Branch A 64 - - Branch B 96 128 - Branch C 16 - 32 Branch D 32 - - 
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表 2 神经网络中数据尺寸变化
层名称 数据尺寸 Input 6 000×1 1_Conv1D 3 000×64 2_Conv1D 1 500×128 Max pooling 500×128 1d-MCL 1 500×256 1d-MCL 2 500×256 1d-MCL 3 500×256 Global Average Pooling 256×1 Dropout 256×1 FC/Output 10×1
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表 3 试验数据集组成
故障类型 电机负载/hp 故障尺寸/mm 标签编号 正常 0/1/2/3 - 0 0.177 8 1 滚珠故障 0/1/2/3 0.355 6 2 0.533 4 3 0.177 8 4 内圈故障 0/1/2/3 0.355 6 5 0.533 4 6 0.177 8 7 外圈故障 0/1/2/3 0.355 6 8 0.533 4 9
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表 4 不同训练集与测试集比例对诊断结果的影响
训练集与测试集比例 诊断准确率/% 9∶1 99.98 8∶2 99.94 7∶3 99.78 6∶4 99.65 5∶5 99.43
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表 5 不同模型的滚动轴承故障诊断结果
数据集 1D-MCNN诊断准确率/% SVM诊断准确率/% BP网络诊断准确率/% RNN诊断准确率/% 0 hp 99.91 83.33 81.18 85.29 1 hp 99.82 95.61 82.94 98.53 2 hp 99.78 86.84 76.18 98.82 3 hp 99.91 93.86 87.06 99.41
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