A Method for Fault Diagnosis of Fuel Injector of Diesel Engine Based on Res-CNN and Fuel Pressure Wave

被引：0
作者：
Jin Y. [1 ]
Qiao X. [1 ]
Gu C. [1 ]
Guo H. [2 ]
Ning C. [3 ]
机构：
[1] Department of Vehicle Engineering, Army Academy of Armored Forces, Beijing
[2] Unit 66407 of Chinese PLA, Beijing
[3] Institute of Military Engineering and Technology, Institute of System Engineering, Academy of Military Science, Beijing
来源：
Qiche Gongcheng/Automotive Engineering | 2021年 / 43卷 / 06期
关键词：
Convolutional neural network; Deep learning; Diesel engine; Fault diagnosis; Fuel injector;
D O I：
10.19562/j.chinasae.qcgc.2021.06.019
中图分类号：
学科分类号：
摘要：
The working quality of the fuel injection system influences the working process and performance of diesel engine directly. Due to the difficulty in recognizing the characteristic points in fuel pressure wave automatically by using fuel pressure wave for fault diagnosis, the on-line fault diagnosis is influenced. This paper proposes a fuel injector fault diagnosis method by deep learning image recognition theory. Experiments are conducted on a fuel injection pump test bench to simulate typical faults, and the fuel pressure wave of high-pressure fuel pipe is measured. The fuel pressure wave characteristics and laws under different fault conditions are analyzed. A deep residual CNN network (Res-CNN) model is built to detect and verify the faults, with the one-dimension fuel pressure wave signal as the input, and the learning process of fault characteristics is analyzed visually. The results show that the model has higher diagnostic accuracy than the traditional method, which verifies the feasibility of direct application of fuel pressure wave image recognition method for on-line real-time monitoring. © 2021, Society of Automotive Engineers of China. All right reserved.
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页码：943 / 951
页数：8
相关论文
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