基于双域特征分析的活塞式航空煤油发动机爆震强度识别

刘娜; 徐胤泽; 胡春明; 杨明堂; 宋玺娟; 杜春媛

doi:10.13224/j.cnki.jasp.20250133

基于双域特征分析的活塞式航空煤油发动机爆震强度识别

doi: 10.13224/j.cnki.jasp.20250133

刘娜^1,,
徐胤泽¹,
胡春明^{1, 2,*, ,},
杨明堂²,
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:
刘娜（1980-），女，工程师，硕士，主要从事小型动力系统智能控制的研究。E-mail：tjliuna@tju.edu.cn

通讯作者:
胡春明（1967-），男，研究员，博士，主要从事航空发动机及其智能控制的研究。E-mail：cmhu@tju.edu.cn

中图分类号: V234⁺.1
计量
- 文章访问数: 296
- HTML浏览量: 176
- PDF量: 39
- 被引次数: 0
出版历程
- 收稿日期: 2025-03-17
- 网络出版日期: 2026-06-11

Knock intensity identification of piston aviation kerosene engine based on dual-domain feature analysis

Liu Na^1
,,
Xu Yinze¹,
Hu Chunming^{1, 2,*
, ,},
Yang Mingtang²,
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:
为了提升对活塞式航空煤油发动机中爆震强度的识别准确性，搭建了专用试验台架并开展燃烧试验，采集了多种工况下的缸内压力信号，从中提取爆震信息。采用小波包变换对缸压信号进行分解，并通过能量熵分析提取了表征爆震等级的子频带分量，发现爆震特征频带集中在7.5～18.75 kHz。基于此频带重构爆震信息，从时域和频域中提取了22个爆震特征指标，形成双域爆震特征图像。为精准识别爆震强度，分别构建了多层感知机（MLP）和卷积神经网络（CNN）模型，以双域爆震特征指标和图像作为输入参数，并在4组工况下进行验证。结果表明：两种模型均具有较高的识别精度，其中CNN模型的平均识别精度达93.46%，较MLP模型的88.50%高出4.96%，验证了CNN模型在爆震强度识别中的准确性与合理性。
- 活塞式航空煤油发动机 /
- 爆震强度识别 /
- 小波包理论 /
- 双域特征分析 /
- 卷积神经网络
Abstract:
To improve the accuracy of recognizing knock intensity in aviation kerosene piston engines, a dedicated test bench was built, combustion tests were conducted, and cylinder pressure signals under various operating conditions were collected to extract knock information. Wavelet packet transform was applied to decompose the pressure signals, and energy entropy analysis was used to identify sub-band components effectively characterizing knock levels. The knock feature sub-band for this engine was found to be concentrated within the 7.5—18.75 kHz range. Knock information within this band was reconstructed, and 22 knock feature indicators were extracted from the time and frequency domains, forming a bi-domain knock feature image. To accurately identify and analyze knock intensity, both a multilayer perceptron (MLP) model and a convolutional neural network (CNN) model were developed. These models used the dual-domain knock feature indicators and images as input parameters and were validated under four different operating conditions. The results demonstrated high recognition accuracy for both models, with the CNN model achieving an average accuracy of 93.46%, surpassing the MLP model’s 88.50% by 4.96%. This highlighted the CNN model’s superior accuracy and generalization capability for knock intensity recognition in aviation kerosene engines.
- piston aviation kerosene engine /
- knock intensity identification /
- wavelet packet theory /
- dual-domain feature analysis /
- convolutional neural network

HTML

图 1 发动机试验台架系统示意图

Figure 1. Schematic diagram of engine test stand system

下载: 全尺寸图片幻灯片

图 2 典型爆震循环的缸压曲线

Figure 2. Cylinder pressure curves for typical knock cycles

下载: 全尺寸图片幻灯片

图 3 6级小波包分解频带结构图（单位：kHz）

Figure 3. Six-level wavelet packet decomposition band structure diagrams （unit：kHz）

下载: 全尺寸图片幻灯片

图 4 非爆震工况下的小波包变换子频带信号

Figure 4. Wavelet packet transform subband signals for non-knock conditions

下载: 全尺寸图片幻灯片

图 5 轻度爆震工况下的小波包变换子频带信号

Figure 5. Wavelet packet transformed sub-band signals under mild knock conditions

下载: 全尺寸图片幻灯片

图 6 中度爆震工况下的小波包变换子频带信号

Figure 6. Wavelet packet transformed subband signals for moderate knock conditions

下载: 全尺寸图片幻灯片

图 7 严重爆震工况下的小波包变换子频带信号

Figure 7. Wavelet packet transformed subband signals under severe knock conditions

下载: 全尺寸图片幻灯片

图 8 不同爆震等级下各子频带能量熵分布及能量占比

Figure 8. Distribution of energy entropy and energy share of each subband under different knock levels

下载: 全尺寸图片幻灯片

图 9 不同爆震等级经爆震窗口处理后爆震特征频带时域图

Figure 9. Time-domain plots of the characteristic frequency bands of the knock processed by the knock window at different knock levels

下载: 全尺寸图片幻灯片

图 10 爆震循环下的时频域特征指标

Figure 10. Time-frequency domain characterization metrics under knock cycling

下载: 全尺寸图片幻灯片

图 11 不同爆震等级下的双域特征时频图

Figure 11. Time-frequency plots of dual-domain features at different knock levels

下载: 全尺寸图片幻灯片

图 12 多层感知机神经网络模型结构

Figure 12. Multilayer perceptron neural network model structure

下载: 全尺寸图片幻灯片

图 13 MLP模型设计与基本步骤

Figure 13. MLP model design and basic steps

下载: 全尺寸图片幻灯片

图 14 MLP模型样本训练结果

Figure 14. Sample training results of MLP model

下载: 全尺寸图片幻灯片

图 15 CNN模型基本结构

Figure 15. CNN model basic structure

下载: 全尺寸图片幻灯片

图 16 CNN模型设计与基本步骤

Figure 16. CNN model design and basic steps

下载: 全尺寸图片幻灯片

图 17 CNN模型样本训练结果

Figure 17. Sample training results of CNN model

下载: 全尺寸图片幻灯片

图 18 MLP模型对不同工况样本的爆震强度识别结果

Figure 18. Knock intensity identification results of MLP model for different operating condition samples

下载: 全尺寸图片幻灯片

图 19 MLP模型对不同工况样本爆震强度识别的混淆矩阵

Figure 19. Confusion matrix of MLP model for knock intensity identification of samples under different operating conditions

下载: 全尺寸图片幻灯片

图 20 CNN模型对不同工况样本的爆震强度识别结果

Figure 20. Knock intensity identification results of CNN model for different operating condition samples

下载: 全尺寸图片幻灯片

图 21 CNN模型对不同工况样本爆震强度识别的混淆矩阵

Figure 21. Confusion matrix of CNN model for knock intensity identification of samples under different operating conditions

下载: 全尺寸图片幻灯片

图 22 不同模型爆震强度识别混淆矩阵

Figure 22. Confusion matrix of different models knock intensity identification

下载: 全尺寸图片幻灯片

表 1 航空煤油发动机基本结构参数

Table 1. Basic structural parameters of aviation kerosene engine

参数	数值或说明
活塞行程/mm	83
气缸直径/mm	100
排量/mL	650
压缩比	9∶1
曲柄连杆长度/mm	142.56
气门最大升程/mm	9
气门数	4
火花塞数	2
活塞形状	偏心碗形状
燃料类型	航空煤油（RP-3）
喷油方式	压缩空气辅助直喷

下载: 导出CSV

表 2 发动机爆震试验工况基本参数

Table 2. Basic parameters of the engine knock test conditions

参数	数值或说明
转速/（r/min）	2500～6500
节气门开度/%	20～50
点火提前角/（°）	上止点前22～39
喷油时刻/（°）	−60
喷油脉宽/ms	4～7
喷射策略	油气同步喷射
点火策略	双火花塞同步点火
过量空气系数	0.85
冷却水温/℃	90

下载: 导出CSV

表 3 不同工况下爆震试验中各爆震等级数量

Table 3. Number of each knock level in the knock test under different operating conditions

参数	数值
参数	工况1	工况2	工况3	工况4	工况5
转速/（r/min）	3500
点火角/（°）	27	30	33	36	39
采样频率/kHz	240
轻度爆震/个循环	15	31	79	101	112
中度爆震/个循环	5	12	33	46	54
严重爆震/个循环	3	9	25	32	38
总计/个循环	23	52	137	179	204

下载: 导出CSV

表 4 圆柱形燃烧室不同共振模态下的对照频率

Table 4. Control frequencies of cylindrical combustion chambers in different resonant modes

共振模态	（m,n）	α_m,n	f_m,n/kHz
1阶切向	（1,0）	1.84	5.86
2阶切向	（2,0）	3.05	9.72
3阶切向	（3,0）	4.20	13.30
4阶切向	（4,0）	5.33	16.90
5阶切向	（5,0）	6.42	20.40
1阶径向	（0,1）	3.83	12.20

下载: 导出CSV

表 5 不同发动机负荷工况下的爆震试验测试集样本

Table 5. Sample test set of knock tests under different engine loading conditions

组别	转速/（r/min）	点火角/（°）	节气门开度/%	循环数	爆震数	爆震率/%
1	2500	30	20	334	62	18.56
2	2500	33	20	363	105	28.93
3	4500	30	40	389	83	21.34
4	6500	32	50	366	113	30.87

下载: 导出CSV

表 6 不同模型爆震强度识别精度

Table 6. Knock intensity identification accuracy of different models %

爆震等级	MLP	CNN
非爆震	88.15	93.30
轻度爆震	88.21	92.92
中度爆震	91.18	94.12
严重爆震	91.84	97.96
平均识别精度	88.50	93.46

下载: 导出CSV

参考文献(27)

[1]	王宝义. 我国低空经济的技术经济范式分析与发展对策[J]. 中国流通经济, 2024, 38(9): 14-26. Wang Baoyi. The technological and economic paradigm analysis and countermeasures for developing low altitude economy in China[J]. China Business and Market, 2024, 38(9): 14-26. (in Chinese doi: 10.14089/j.cnki.cn11-3664/f.2024.09.002 Wang Baoyi. The technological and economic paradigm analysis and countermeasures for developing low altitude economy in China[J]. China Business and Market, 2024, 38(9): 14-26. (in Chinese) doi: 10.14089/j.cnki.cn11-3664/f.2024.09.002
[2]	张红兴. 对置活塞二冲程内燃机用于无人机的研究[D]. 哈尔滨: 哈尔滨工业大学, 2018. Zhang Hongxing. Study on an opposed piston two stroke internal combustion engine for unmanned aerial vehicle[D]. Harbin: Harbin Institute of Technology, 2018. (in Chinese Zhang Hongxing. Study on an opposed piston two stroke internal combustion engine for unmanned aerial vehicle[D]. Harbin: Harbin Institute of Technology, 2018. (in Chinese)
[3]	胡强, 余双, 史开源, 等. 浅谈航空重油活塞式发动机[J]. 航空动力, 2020(5): 32-35. Hu Qiang, Yu Shuang, Shi Kaiyuan, et al. Discussion on the aviation heavy fuel piston engines[J]. Aerospace Power, 2020(5): 32-35. (in Chinese Hu Qiang, Yu Shuang, Shi Kaiyuan, et al. Discussion on the aviation heavy fuel piston engines[J]. Aerospace Power, 2020(5): 32-35. (in Chinese)
[4]	Ding Shuiting, Ma Qinglin, Qiu Tian, et al. An engine-level safety assessment approach of sustainable aviation fuel based on a multi-fidelity aerodynamic model[J]. Sustainability, 2024, 16(9): 3814. doi: 10.3390/su16093814
[5]	Napolitano P, Jimenez I, Pla B, et al. Knock recognition based on vibration signal and Wiebe function in a heavy-duty spark ignited engine fueled with Methane[J]. Fuel, 2022, 315: 122957. doi: 10.1016/j.fuel.2021.122957
[6]	周浩. 直喷发动机的爆震分析研究[D]. 天津: 天津大学, 2014. Zhou Hao. Investigation on knock analysis of direct injection engine[D]. Tianjin: Tianjin University, 2014. (in Chinese Zhou Hao. Investigation on knock analysis of direct injection engine[D]. Tianjin: Tianjin University, 2014. (in Chinese)
[7]	Pla B, De La Morena J, Bares P, et al. An unsupervised machine learning technique to identify knock from a knock signal time-frequency analysis[J]. Measurement, 2023, 211: 112669. doi: 10.1016/j.measurement.2023.112669
[8]	Borg J M, Cheok K C, Saikalis G, et al. Wavelet-based knock detection with fuzzy logic[C]//2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications. Piscataway, US: IEEE, 2005: 26-31.
[9]	李宁, 周瑞. 基于非线性小波变换的汽油机爆震强度识别[J]. 内燃机学报, 2018, 36(1): 83-89. Li Ning, Zhou Rui. Knock intensity identification for a gasoline engine based on nonlinear wavelet transform[J]. Transactions of Csice, 2018, 36(1): 83-89. (in Chinese doi: 10.16236/j.cnki.nrjxb.201801011 Li Ning, Zhou Rui. Knock intensity identification for a gasoline engine based on nonlinear wavelet transform[J]. Transactions of Csice, 2018, 36(1): 83-89. (in Chinese) doi: 10.16236/j.cnki.nrjxb.201801011
[10]	孙久岭. 天然气发动机爆震燃烧特性及识别算法研究[D]. 北京: 北京交通大学, 2022. Sun Jiuling. Research on knock combustion characteristics and identification algorithm of natural gas engine[D]. Beijing: Beijing Jiaotong University, 2022. (in Chinese Sun Jiuling. Research on knock combustion characteristics and identification algorithm of natural gas engine[D]. Beijing: Beijing Jiaotong University, 2022. (in Chinese)
[11]	Kefalas A, Ofner A B, Pirker G, et al. Detection of knocking combustion using the continuous wavelet transformation and a convolutional neural network[J]. Energies, 2021, 14(2): 439. doi: 10.3390/en14020439
[12]	Hosseini M, Chitsaz I. Knock probability determination employing convolutional neural network and IGTD algorithm[J]. Energy, 2023, 284: 129282. doi: 10.1016/j.energy.2023.129282
[13]	Pla B N, Bares P, Jiménez I, et al. A fuzzy logic map-based knock control for spark ignition engines[J]. Applied Energy, 2020, 280: 116036. doi: 10.1016/j.apenergy.2020.116036
[14]	杨建国, 刘晓峰, 林波. 基于DWT的汽油机爆震特征提取及爆震强度的判定[J]. 内燃机学报, 2003, 21(3): 233-238. Yang Jianguo, Liu Xiaofeng, Lin Bo. DWT based knock detection and knock intensity judgment for a gasoline engine[J]. Transactions of Csice, 2003, 21(3): 233-238. (in Chinese doi: 10.3321/j.issn:1000-0909.2003.03.008 Yang Jianguo, Liu Xiaofeng, Lin Bo. DWT based knock detection and knock intensity judgment for a gasoline engine[J]. Transactions of Csice, 2003, 21(3): 233-238. (in Chinese) doi: 10.3321/j.issn:1000-0909.2003.03.008
[15]	Molinaro F, Castanie F, Denjean A. Knocking recognition in engine vibration signal using the wavelet transform[C]//Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis. Piscataway, US: IEEE, 1992: 353-356.
[16]	薛劲梓, 胡春明, 刘娜, 等. 基于小波包能熵谱的爆震特征频带及强度分析[J]. 中南大学学报(自然科学版), 2022, 53(6): 2092-2101. Xue Jinzi, Hu Chunming, Liu Na, et al. Frequency band and intensity analysis of knock characteristics based on wavelet packet energy entropy spectrum[J]. Journal of Central South University (Science and Technology), 2022, 53(6): 2092-2101. (in Chinese doi: 10.11817/j.issn.1672-7207.2022.06.011 Xue Jinzi, Hu Chunming, Liu Na, et al. Frequency band and intensity analysis of knock characteristics based on wavelet packet energy entropy spectrum[J]. Journal of Central South University (Science and Technology), 2022, 53(6): 2092-2101. (in Chinese) doi: 10.11817/j.issn.1672-7207.2022.06.011
[17]	Naber J, Blough J R, Frankowski D, et al. Analysis of combustion knock metrics in spark-ignition engines[R]. Detroit, US: Sae World Congress and Exhibition, 2006.
[18]	盛敬, 魏民祥, 刘锐. 基于经验模态分解的煤油发动机爆震因子计算[J]. 内燃机工程, 2013, 34(5): 29-32, 37. Sheng Jing, Wei Minxiang, Liu Rui. Research on knocking factor calculating method based on empirical mode decomposition algorithm for kerosene engine[J]. Chinese Internal Combustion Engine Engineering, 2013, 34(5): 29-32, 37. (in Chinese doi: 10.13949/j.cnki.nrjgc.2013.05.009 Sheng Jing, Wei Minxiang, Liu Rui. Research on knocking factor calculating method based on empirical mode decomposition algorithm for kerosene engine[J]. Chinese Internal Combustion Engine Engineering, 2013, 34(5): 29-32, 37. (in Chinese) doi: 10.13949/j.cnki.nrjgc.2013.05.009
[19]	张德丰. MATLAB小波分析[M]. 北京: 机械工业出版社, 2009.
[20]	Pan Yue, Zhang Limao, Wu Xianguo, et al. Structural health monitoring and assessment using wavelet packet energy spectrum[J]. Safety Science, 2019, 120: 652-665. doi: 10.1016/j.ssci.2019.08.015
[21]	Hudson C, Gao X, Stone R. Knock measurement for fuel evaluation in spark ignition engines[J]. Fuel, 2001, 80(3): 395-407. doi: 10.1016/S0016-2361(00)00080-6
[22]	何正友, 蔡玉梅, 钱清泉. 小波熵理论及其在电力系统故障检测中的应用研究[J]. 中国电机工程学报, 2005, 25(5): 38-43. He Zhengyou, Cai Yumei, Qian Qingquan. A study of wavelet entropy theory and its application in electric power system fault detection[J]. Proceedings of the CSEE, 2005, 25(5): 38-43. (in Chinese doi: 10.3321/j.issn:0258-8013.2005.05.007 He Zhengyou, Cai Yumei, Qian Qingquan. A study of wavelet entropy theory and its application in electric power system fault detection[J]. Proceedings of the CSEE, 2005, 25(5): 38-43. (in Chinese) doi: 10.3321/j.issn:0258-8013.2005.05.007
[23]	张炜博. 基于时频分析与非线性熵的水电机组智能故障诊断与状态趋势预测研究[D]. 武汉: 华中科技大学, 2019. Zhang Weibo. Study on intelligent fault diagnosis and state tendency prediction of hydroelectric generator units based on time-frequency analysis and nonlinear entropy[D]. Wuhan: Huazhong University of Science and Technology, 2019. (in Chinese Zhang Weibo. Study on intelligent fault diagnosis and state tendency prediction of hydroelectric generator units based on time-frequency analysis and nonlinear entropy[D]. Wuhan: Huazhong University of Science and Technology, 2019. (in Chinese)
[24]	Donahue J, Jia Y, Vinyals O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition[R]. Beijing: 31st International Conference on Machine Learning, 2014.
[25]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386
[26]	吴正文. 卷积神经网络在图像分类中的应用研究[D]. 成都: 电子科技大学, 2015. Wu Zhengwen. Application research of convolution neural network in image classification[D]. Chengdu: University of Electronic Science and Technology of China, 2015. (in Chinese Wu Zhengwen. Application research of convolution neural network in image classification[D]. Chengdu: University of Electronic Science and Technology of China, 2015. (in Chinese)
[27]	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.