吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 417-424.doi: 10.13229/j.cnki.jdxbgxb20210777

• 车辆工程·机械工程 • 上一篇    

基于卷积神经网络的柴油机失火故障实时诊断

高文志1(),王彦军1,王欣伟2,张攀1,李勇1,董阳1   

  1. 1.天津大学 内燃机燃烧学国家重点实验室,天津 300072
    2.潍柴动力股份有限公司,山东 潍坊 261041
  • 收稿日期:2021-08-12 出版日期:2022-02-01 发布日期:2022-02-17
  • 作者简介:高文志(1965-),男,教授,博士.研究方向:发动机及动力总成NVH.E-mail:gaowenzhi@tju.edu.cn
  • 基金资助:
    内燃机可靠性国家重点实验室开放基金项目(SKLER-202010)

Real⁃time diagnosis for misfire fault of diesel engine based on convolutional neural network

Wen-zhi GAO1(),Yan-jun WANG1,Xin-wei WANG2,Pan ZHANG1,Yong LI1,Yang DONG1   

  1. 1.State Key Laboratory of Engines,Tianjin University,Tianjin 300072,China
    2.Weichai Power Co. ,Ltd. ,Weifang 261041,China
  • Received:2021-08-12 Online:2022-02-01 Published:2022-02-17

摘要:

针对发动机的失火故障,提出了一种基于卷积神经网络(CNN)的失火诊断方法,构建了基于STM32单片机的柴油机失火故障实时诊断系统。通过STM32CubeMX软件将柴油机失火故障诊断的卷积神经网络写入到单片机中,在试验过程中利用单片机的定时器输入捕获功能采集柴油机的转速信号,且将上止点信号作为转速采集的触发信号,将采集到的转速进行预处理作为卷积神经网络的输入。通过柴油机台架试验证明,所建立的柴油机失火实时诊断系统在较宽的转速与负荷工况下有较高的诊断准确率。

关键词: 柴油机, 失火故障诊断, 卷积神经网络, STM32单片机

Abstract:

Aiming at diagnosing the misfire fault of engine, a method of misfire diagnosis based on convolutional neural network(CNN) is proposed. The real-time diagnosis system for misfire fault detection of diesel engine is constructed based on STM32 single chip microcomputer. The convolutional neural network for misfire fault diagnosis of diesel engine is written in Microcontroller Unit(MCU) based on STM32CubeMX software. In the experiment the speed signal is collected by using the timer input capture function of single chip microcomputer, and the top dead center signal works as the trigger signal for speed acquisition. The collected speed data is preprocessed and then used as the input of CNN. The test results in the diesel engine bench show that the real-time misfire diagnosis system has high diagnostic accuracy under wide range of speed and load conditions.

Key words: diesel engine, misfire diagnosis, convolutional neural network, STM32 single chip microcomputer

中图分类号: 

  • TK428

图1

转速波动图"

表1

CNN的结构参数"

序号

神经网

络层

核尺寸核数量步长激活 函数补零输出规模
1输入层/////
2卷积层C16×2161×1ReluSAME
3池化层S13×2163×2/SAME
4卷积层C26×2321×1ReluSAME
5池化层S22×2322×2/SAME
6平铺层/////1280
7全连接层11/Relu/60
8输出层11/Softmax/7

表2

发动机参数"

发动机参数数值
气缸数6
气缸的布置形式立式直列
缸径/mm102
行程/mm118
总排量/L5.8
压缩比17.5
发火顺序1-5-3-6-2-4
转速范围/(r·min-1800~2200
标定功率/kW85

图2

测试结果"

图3

验证数据的测试结果"

表3

测试准确率"

负载/(N·m)转速/(r·min-1准确率/%
空载100099.61
180099.77
150100099.50
140099.85
180099.77
300100099.83
180099.91

图4

诊断系统框图"

表4

台架试验结果 (%)"

负荷/(N·m)转速/(r·min-1
90015001900
空载99.29100.0099.29
75100.00100.0099.29
150100.00100.00100.00
300100.00100.00100.00
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