| 引用本文: | 徐 铭1,徐 航1,陈 洋2,束正华1,胡江颖1,汪 诚1.基于卷积残差编码网络的水泥辊压机故障诊断(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（2）：133-138 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 为提高水泥生产效率,避免因主要机械设备故障导致的生产停止,需要对水泥生产线中的主机设备辊
压机进行故障诊断。 方法 提出卷积残差 Transformer 编码网络,此模型第一部分是包含 4 个卷积层的卷积块,每个
卷积层中包含卷积、池化、批规范化和激活函数,用以提取输入数据的局部特征;对提取到的特征增加残差后输入
Transformer 编码模块,Transformer 编码模块包含多头注意力机制和前馈神经网络,用以对输入的特征进行再次提
取,得到编码的输出后再次增加残差;最后经过全连接层和 Softmax 层进行分类以实现对故障的诊断。 结果 在凯斯
西储大学公开轴承数据集进行模型可用性的初步验证,然后在辊压机上安装传感器收集正常运行和故障运行的振
动数据作为实证数据集,其中凯斯西储大学测试集预测准确率为 99. 75%,对辊压机数据测试集的识别准确率为
96. 55%,故障的查全率达到 98. 32%。 结论 使用卷积提取故障数据特征,然后用注意力机制突出重要特征的方法
能对辊压机实际生产中的运行状态进行有效的判断,特别是对故障状态的识别率更高。 |
| 关键词: 辊压机 故障诊断 卷积网络 Transformer 编码 |
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| Fault Diagnosis of Cement Roller Press Based on Convolutional Residual Coding Network |
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XU Ming1,XU Hang1,CHEN Yang2, SHU Zhenghua1, HU Jiangying1, WANG,Cheng1
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1. Anhui Zhizhi Engineering Technology Co. Ltd. Wuhu 241000 Anhui China
2. Shanghai Zhizhi Technology Co. Ltd. Shanghai 201801 China
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| Abstract: |
| Objective To improve the efficiency of cement production and prevent production stoppages caused by major
mechanical equipment failures fault diagnosis for roller presses in cement production lines is essential. Methods A
convolutional residual Transformer coding network was proposed. The first part of this model was a convolution block
containing four convolutional layers. Each convolutional layer contained convolution pooling batch normalization and
activation functions to extract local features from the input data. The extracted features were then fed into a Transformer
coding module after adding residuals. This Transformer coding module incorporated a multi-head attention mechanism and
a feedforward neural network to further extract features from the input. After obtaining the encoded output the residuals
were added again. The difference was finally classified through the fully connected layer and softmax layer to achieve fault
diagnosis. Results A preliminary verification of the model usability was carried out on the public bearing data set of Case
Western Reserve University and then sensors were installed on the roller press to collect vibration data of normal operation
and fault operation as an empirical data set. The prediction accuracy on the Case Western Reserve University test set was
99. 75% while the identification accuracy on the roller press data test set was 96. 55% with a fault recall rate of 98. 32%. Conclusion The method of using convolution to extract fault data features and employing an attention
mechanism to highlight important features enables effective assessment of the operational status of the roller press in actual
production particularly achieving a higher recognition rate for fault states. |
| Key words: roller press fault diagnosis convolutional network Transformer coding |
