基于小波去噪和卷积神经网络的发动机爆震识别

胡春明; 刘铮; 刘娜; 宋玺娟; 杜春媛

doi:10.13224/j.cnki.jasp.20220414

基于小波去噪和卷积神经网络的发动机爆震识别

doi: 10.13224/j.cnki.jasp.20220414

胡春明^{1, 2},
刘铮^{1, 2},
刘娜¹,
宋玺娟¹,
杜春媛¹

1.
天津大学内燃机研究所，天津 300072
2.
天津大学机械工程学院，天津 300072

基金项目: 国家自然科学基金（51476112）

详细信息

作者简介:
胡春明（1967-），男，研究员，博士，主要从事内燃机及混合动力智能控制研究

中图分类号: V234.1
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- 文章访问数: 13
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- PDF量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-10

Engine knock recognition based on wavelet domains denoising and convolutional neural network

HU Chunming^{1, 2},
LIU Zheng^{1, 2},
LIU Na¹,
SONG Xijuan¹,
DU Chunyuan¹

1.
Internal Combustion Engine Research Institute，Tianjin University，Tianjin 300072，China
2.
School of Mechanical Engineering，Tianjin University，Tianjin 300072，China

摘要

摘要:
在活塞式航空煤油发动机上进行爆震试验研究，首先使用小波去噪对发动机缸压信号进行噪声提取，然后对0°～45°曲轴转角内的噪声信号进行快速傅里叶变换将一维时域噪声信号展开成二维时频域特征图，最后将特征图输入到训练好的卷积神经网络（convolutional neural networks, CNN）中进行爆震识别。验证结果表明：轻微和严重爆震都会在10°～30°曲轴转角内产生幅值较大噪声信号，与无爆震循环的时频域特征图有明显区别；在爆震特征提取上小波去噪要优于带通滤波，在爆震特征识别上CNN方法要优于支持向量机（support vector machine, SVM）方法；小波去噪和CNN结合的爆震识别方法对发动机4种不同运行工况的爆震识别准确率都能达到91%以上；小波去噪结合CNN方法对爆震循环的查准率为83.16%，查全率高达98.79%，能够准确的识别出发动机的爆震循环。
- 爆震识别 /
- 活塞式航空煤油发动机 /
- 小波分析 /
- 小波去噪 /
- 深度学习 /
- 卷积神经网络
Abstract:
Based on the method of wavelet domain denoising, the noise signals from in-cylinder pressure were extracted, at crank angle of 0°—45°, fast Fourier transform was used for simultaneous analysis of the noise signal in the time and frequency domains, then the feature map was outputted. The map was inputted into convolutional neural network (CNN) for identifying different features in order to distinguish non-knock and knock. The knock test was conducted on a direct injection engine fueled with aviation kerosene. The result revealed that: the time-frequency map was significantly different between knock and non-knock, because slight knocking and severe knocking both produced large-amplitude noise signals within crank angle of 10°—30°. Wavelet denoising was better than bandpass filtering in knocking feature extraction, while CNN was better than Support Vector Machine (SVM) in knocking feature recognition; under four different operating conditions, the knock recognition accuracy was all over 91% by wavelet domain denoising combining with CNN method; the precision and recall of the knock were 83.16% and 98.79%, respectively.
- knock recognition /
- piston aviation kerosene engine /
- wavelet analysis /
- wavelet domain denoising /
- deep learning /
- convolutional neural network

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图 1 台架试验系统示意图

1测试机；2离合器；3发动机；4爆震传感器；5缸压传感器；6电荷放大器；7转速传感器；8数据采集卡；9燃烧分析仪；10发动机控制单元；11上位机。

Figure 1. Schematic diagram of bench test system

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图 2 非爆震和轻微爆震缸压对比图

Figure 2. Comparison chart of cylinder pressure with non-knock and slight knock

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图 3 小波变换阈值去噪法流程图

Figure 3. Flow chart of wavelet threshold denoising method

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图 4 非爆震工况小波分解

Figure 4. Wavelet decomposition for non-knock conditions

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图 5 爆震工况小波分解

Figure 5. Wavelet decomposition for knock conditions

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图 6 原始与重构信号对比

Figure 6. Comparison of original and reconstructed signal

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图 7 高频噪声信号

Figure 7. High frequency noise signal

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图 8 非爆震与爆震工况能量熵随频率变化对比

Figure 8. Comparison of energy entropy with frequency under non-knock and knock conditions

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图 9 发动机爆震与非爆震工况的特征时频图对比

Figure 9. Comparison of characteristic time-frequency maps under non-knock and knock conditions

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图 10 CNN网络结构图

Figure 10. CNN network structure diagram

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图 11 小波去噪结合CNN识别结果的混淆矩阵

Figure 11. Confusion matrix of wavelet denoising combined with CNN

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表 1 单缸活塞式航空煤油发动机试验参数

Table 1. Test parameters of single cylinder piston aviation kerosene engine

参数	数值或说明
排量/L	0.65
缸径/mm	100
连杆长度/mm	142.56
行程/mm	83
压缩比	9
气门数	2
活塞顶端形状	偏心球形
喷油方式	低压空气辅助直喷
冷却方式	缸头水冷、缸体风冷

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表 2 发动机爆震试验典型工况参数

Table 2. Typical working parameters of engine knock test

参数	数值
转速/（r/min）	3500
节气门开度/%	30
点火提前角/（°）	27-39
喷油提前角/（°）	60
过量空气系数	1.0
冷却水温度/℃	80

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表 3 典型工况下爆震情况

Table 3. Knock situation under typical operating conditions

转速/（r/min）	点火提前角/（°）	循环数	爆震数	爆震率/%
3500	27	370	22	5.95
	30	367	54	14.71
	33	342	133	38.89
	36	340	200	58.82
	39	338	180	53.25

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表 4 CNN架构

Table 4. CNN architecture

层	核大小	输出大小	参数说明
输入层		（450, 100, 1）
卷积层	2×2	（446, 96, 64）	ReLU+SAME
池化层	2×2	（223, 48, 64）	vaild
卷积层	3×3	（221, 46, 128）	ReLU+SAME
池化层	2×2	（110, 23, 128）	vaild
卷积层	3×3	（108, 21, 256）	ReLU+SAME
池化层	2×2	（54, 10, 256）	vaild
展开层		138240
密集层		128	ReLU
Dropout		128	50%
密集层		64	ReLU
Dropout		64	50%
密集层		32	ReLU
Dropout		32	50%
输出层		2	softmax

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表 5 测试工况参数说明

Table 5. Test condition parameter description

组别	点火提前角/（°）	转速/（r/min）	节气门开度/%	循环数	爆震数	爆震率/%
第1组	30	2500	20	324	58	17.9
第2组	30	4500	40	328	60	18.29
第3组	33	2500	20	391	117	29.92
第4组	33	4500	40	359	178	49.58

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表 6 不同分类模型的识别正确率结果对比

Table 6. Comparison of recognition accuracy results of different classification models

模型		正确率/%
模型		第1组	第2组	第3组	第4组
CNN	a	93.2	94.21	91.3	95.82
CNN	b	87.65	88.11	88.23	87.74
SVM	a	80.86	81.1	75.7	76.6
SVM	b	79.01	78.05	73.66	74.37

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