Diesel Engine Misfire Diagnosis with Deep Convolutional Neural Network Using Dropout and Batch Normalization

被引：4

作者：

Zhang K. ^{[1
]}

Tao J. ^{[1
]}

Qin C. ^{[1
]}

Li W. ^{[1
]}

Liu C. ^{[1
]}

机构：

[1] State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai

来源：

Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2019年 / 53卷 / 08期

关键词：

Batch normalization; Deep convolutional neural network; Dropout; Misfire diagnosis; Noise environment;

D O I：

10.7652/xjtuxb201908021

中图分类号：

学科分类号：

摘要：

The existing diesel engine misfire fault diagnosis methods require a fine and time-consuming time-frequency feature extraction process, and have low diagnostic accuracy for the actual noise-contained samples. An end-to-end intelligent diagnosis method with deep convolutional neural network using dropout and batch normalization mechanism is proposed. The simulation test of diesel engine misfire fault is conducted at different rotating speeds, and the original cylinder head vibration signals are acquired. The dropout mechanism is introduced to suppress input noise at the input end, and the features of misfire fault are automatically extracted by using one-dimensional convolution. Then the batch characteristic signals of each convolution output are normalized to reduce variance offset. Misfire fault classification is completed based on the multi-classification function. According to the comparative experiment results of different noise environments and evaluation methods, the maximum classification accuracy of the proposed method is up to 100%, and the robustness is better than the methods depending on feature extraction. © 2019, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.

引用

页码：159 / 166

页数：7

共 20 条

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