基于MACNN的柴油机故障诊断方法研究

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摘要高效、准确地故障诊断可以提高柴油机的安全性和可靠性。传统机械故障诊断方法中人工参与程度过高，为识别结果带来诸多不确定性。针对这一问题，论文提出一种基于多重注意力卷积神经网络 (Multiple Attention Convolutional Neural Networks, MACNN)的端到端的故障诊断方法。该方法采用多层卷积神经网络 (Convolutional Neural Networks, CNN) 结合卷积注意力模块 (Convolutional Block Attention Module, CBAM) 对原始时域数据进行特征提取，然后对多维卷积输出特征图进行重组以保留其序列信息，最后直接采用序列注意力机制完成序列特征的学习。经采用实测柴油机缸盖振动信号数据进行验证后表明：面对8分类柴油机故障数据集，MACNN能够达到97.88%的识别准确率，测试100个样本用时仅为0.35s。与现有多种传统故障诊断方法和端到端故障诊断方法相比，均具有更好的诊断效果。

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	程建刚1
	毕凤荣1
	张立鹏2
	李鑫1
	杨晓1
	汤代杰1

关键词 ：多重注意力, 卷积神经网络, 故障诊断, 端到端

Abstract：Efficient and accurate fault diagnosis can improve the safety and reliability of diesel engine. In the traditional mechanical fault diagnosis method, the degree of human participation is too high, which brings high uncertainty to results. To solve this problem, this paper proposes an end-to-end fault diagnosis method based on multiple attention convolutional neural networks (MACNN). In this method, multi-layer convolutional neural networks (CNN) and convolutional block attention module (CBAM) are used to extract features from the original time-domain data, and then the multi-dimensional feature map of convolutional output is recombined to retain its sequence information. Finally, the sequential attention mechanism is directly used to learn the sequence feature. The results show that MACNN can achieve recognition accuracy of 97.88% for eight-class fault data set of diesel engine, and the time taken to test 100 samples is only 0.35 seconds. Compared with other traditional fault diagnosis methods and end-to-end fault diagnosis methods, the proposed MACNN has better diagnosis effect.

Key words： multiple attention convolutional neural networks (CNN) fault diagnosis end to end

收稿日期: 2021-01-20 出版日期: 2022-05-28

引用本文:

程建刚1，毕凤荣1，张立鹏2，李鑫1，杨晓1，汤代杰1. 基于MACNN的柴油机故障诊断方法研究[J]. 振动与冲击, 2022, 41(10): 8-15.
CHENG Jiangang1，BI Fengrong1，ZHANG Lipeng2，LI Xin1，YANG Xiao1，TANG Daijie1. Fault diagnosis method for diesel engines based on MACNN. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(10): 8-15.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I10/8

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