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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

胡春明, 刘铮, 刘娜, 等. 基于小波去噪和卷积神经网络的发动机爆震识别[J]. 航空动力学报, 2024, 39(7):20220414 doi: 10.13224/j.cnki.jasp.20220414
引用本文: 胡春明, 刘铮, 刘娜, 等. 基于小波去噪和卷积神经网络的发动机爆震识别[J]. 航空动力学报, 2024, 39(7):20220414 doi: 10.13224/j.cnki.jasp.20220414
HU Chunming, LIU Zheng, LIU Na, et al. Engine knock recognition based on wavelet domains denoising and convolutional neural network[J]. Journal of Aerospace Power, 2024, 39(7):20220414 doi: 10.13224/j.cnki.jasp.20220414
Citation: HU Chunming, LIU Zheng, LIU Na, et al. Engine knock recognition based on wavelet domains denoising and convolutional neural network[J]. Journal of Aerospace Power, 2024, 39(7):20220414 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
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

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

  • 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。

     

  • 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

    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

    图 3  小波变换阈值去噪法流程图

    Figure 3.  Flow chart of wavelet threshold denoising method

    图 4  非爆震工况小波分解

    Figure 4.  Wavelet decomposition for non-knock conditions

    图 5  爆震工况小波分解

    Figure 5.  Wavelet decomposition for knock conditions

    图 6  原始与重构信号对比

    Figure 6.  Comparison of original and reconstructed signal

    图 7  高频噪声信号

    Figure 7.  High frequency noise signal

    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

    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

    图 10  CNN网络结构图

    Figure 10.  CNN network structure diagram

    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

    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/L0.65
    缸径/mm100
    连杆长度/mm142.56
    行程/mm83
    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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/%
    350027370225.95
    303675414.71
    3334213338.89
    3634020058.82
    3933818053.25
    下载: 导出CSV

    表  4  CNN架构

    Table  4.   CNN architecture

    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
    下载: 导出CSV

    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302500203245817.9
    第2组4500403286018.29
    第3组3325002039111729.92
    第4组45004035917849.58
    下载: 导出CSV

    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/%
    第1组 第2组 第3组 第4组
    CNN a 93.2 94.21 91.3 95.82
    b 87.65 88.11 88.23 87.74
    SVM a 80.86 81.1 75.7 76.6
    b 79.01 78.05 73.66 74.37
    下载: 导出CSV
  • [1] 宗培越,胡春明,王书典. 活塞式航空煤油直喷发动机的爆震控制[J]. 航空动力学报,2018,33(3): 635-641. ZONG Peiyue,HU Chunming,WANG Shudian. Knocking control of direct injection piston-type aviation kerosene engine[J]. Journal of Aerospace Power,2018,33(3): 635-641. (in Chinese

    ZONG Peiyue, HU Chunming, WANG Shudian. Knocking control of direct injection piston-type aviation kerosene engine[J]. Journal of Aerospace Power, 2018, 33(3): 635-641. (in Chinese)
    [2] 周龙保. 内燃机学[M]. 3版. 北京: 机械工业出版社,2011.
    [3] 吴华堂. 船用天然气发动机爆震检测与控制技术研究[D]. 哈尔滨: 哈尔滨工程大学,2021. WU Huatang. Technology research on knock detection and control of marine natural gas engine[D]. Harbin: Harbin Engineering University,2021. (in Chinese

    WU Huatang. Technology research on knock detection and control of marine natural gas engine[D]. Harbin: Harbin Engineering University, 2021. (in Chinese)
    [4] SHAHLARI A J,GHANDHI J B. A comparison of engine knock metrics[R]. Warrendale,US: Small Engine Technology Conference & Exhibition,2012.
    [5] 张力,康健,徐凤雏,等. 基于爆震因子统计特性的爆震燃烧识别方法[J]. 内燃机学报,2011,29(2): 152-156. ZHANG Li,KANG Jian,XU Fengchu,et al. Knock diagnosis based on statistical characteristics of knock factor[J]. Transactions of CSICE,2011,29(2): 152-156. (in Chinese

    ZHANG Li, KANG Jian, XU Fengchu, et al. Knock diagnosis based on statistical characteristics of knock factor[J]. Transactions of CSICE, 2011, 29(2): 152-156. (in Chinese)
    [6] 刘颖,陶建峰,黄武涛,等. 小波包能量与CNN相结合的滚动轴承故障诊断方法[J]. 机械设计与制造,2021(11): 127-131. LIU Ying,TAO Jianfeng,HUANG Wutao,et al. Rolling bearing fault diagnosis method based on the combination of wavelet packet energy and CNN[J]. Machinery Design & Manufacture,2021(11): 127-131. (in Chinese

    LIU Ying, TAO Jianfeng, HUANG Wutao, et al. Rolling bearing fault diagnosis method based on the combination of wavelet packet energy and CNN[J]. Machinery Design & Manufacture, 2021(11): 127-131. (in Chinese)
    [7] 韩璞. 基于振动信号的汽油机爆震特征提取与强度评价研究[D]. 天津: 天津大学,2014. HAN Pu. Research on gasoline engine knock feature extraction and intensity evaluation using vibration signal[D]. Tianjin: Tianjin University,2014. (in Chinese

    HAN Pu. Research on gasoline engine knock feature extraction and intensity evaluation using vibration signal[D]. Tianjin: Tianjin University, 2014. (in Chinese)
    [8] WANG Xuezhong. Electronic radar signal recognition based on wavelet transform and convolution neural network[J]. Alexandria Engineering Journal,2022,61(5): 3559-3569. doi: 10.1016/j.aej.2021.09.002
    [9] 盛立,徐西龙,王维波,等. 基于时频分析和卷积神经网络的微地震事件检测[J]. 中国石油大学学报(自然科学版),2021,45(5): 54-63. SHENG Li,XU Xilong,WANG Weibo,et al. Detection of microseismic events based on time-frequency analysis and convolutional neural network[J]. Journal of China University of Petroleum (Edition of Natural Science),2021,45(5): 54-63. (in Chinese

    SHENG Li, XU Xilong, WANG Weibo, et al. Detection of microseismic events based on time-frequency analysis and convolutional neural network[J]. Journal of China University of Petroleum (Edition of Natural Science), 2021, 45(5): 54-63. (in Chinese)
    [10] 陈德昊,林建恒,衣雪娟,等. 基于小波包时频图特征和卷积神经网络的水声信号分类[J]. 声学技术,2021,40(3): 336-340. CHEN Dehao,LIN Jianheng,YI Xuejuan,et al. Classification of underwater acoustic signals based on time-frequency map features of wavelet packet and convolutional neural network[J]. Technical Acoustics,2021,40(3): 336-340. (in Chinese

    CHEN Dehao, LIN Jianheng, YI Xuejuan, et al. Classification of underwater acoustic signals based on time-frequency map features of wavelet packet and convolutional neural network[J]. Technical Acoustics, 2021, 40(3): 336-340. (in Chinese)
    [11] 陈妍伶,程良伦,吴衡,等. 基于小波系数图和卷积神经网络的太赫兹光谱物质识别[J]. 光谱学与光谱分析,2021,41(12): 3665-3670. CHEN Yanling,CHENG Lianglun,WU Heng,et al. A method of terahertz spectrum material identification based on wavelet coefficient graph[J]. Spectroscopy and Spectral Analysis,2021,41(12): 3665-3670. (in Chinese

    CHEN Yanling, CHENG Lianglun, WU Heng, et al. A method of terahertz spectrum material identification based on wavelet coefficient graph[J]. Spectroscopy and Spectral Analysis, 2021, 41(12): 3665-3670. (in Chinese)
    [12] PENG Cenxin,CHENG Wei,SONG Zihao,et al. A noise-robust modulation signal classification method based on continuous wavelet transform[C]//2020 IEEE 5th Information Technology and Mechatronics Engineering Conference. Piscataway,US: IEEE,2020: 745-750.
    [13] BORG J M,SAIKALIS G,OHO S,et al. Knock signal analysis using the discrete wavelet transform[R]. Warrendale,US: SAE World Congress,2006.
    [14] 刘成材,高青,金英爱,等. 基于小波包分析提取汽油机爆震特征[J]. 内燃机学报,2012,30(2): 161-165. LIU Chengcai,GAO Qing,JIN Ying’ai,et al. Feature extraction in knocking detection of gasoline engine based on wavelet packet transform[J]. Transactions of CSICE,2012,30(2): 161-165. (in Chinese

    LIU Chengcai, GAO Qing, JIN Ying’ai, et al. Feature extraction in knocking detection of gasoline engine based on wavelet packet transform[J]. Transactions of CSICE, 2012, 30(2): 161-165. (in Chinese)
    [15] 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

    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)
    [16] BORG J M,CHEOK K C,SAIKALIS G,et al. Wavelet-based knock detection with fuzzy logic[C]//CIMSA 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications. Piscataway,US: IEEE,2005: 26-31.
    [17] CHO S,PARK J,SONG C,et al. Prediction modeling and analysis of knocking combustion using an improved 0D RGF model and supervised deep learning[J]. Energies,2019,12(5): 844. doi: 10.3390/en12050844
    [18] PETRUCCI L,RICCI F,MARIANI F,et al. Engine knock evaluation using a machine learning approach[R]. Warrendale,US: Conference on Sustainable Mobility,2020.
    [19] MAURYA R K. Knocking and combustion noise analysis[M]//Reciprocating engine combustion diagnostics. Cham,Germany: Springer,2019: 461-542.
    [20] ZHEN Xudong,WANG Yang,XU Shuaiqing,et al. The engine knock analysis - An overview[J]. Applied Energy,2012,92: 628-636. doi: 10.1016/j.apenergy.2011.11.079
    [21] LEE J H,HWANG S H,LIM J S,et al. A new knock-detection method using cylinder pressure,block vibration and sound pressure signals from a SI engine[R]. Warrendale,US: SAE International Fuels and Lubricants Meeting and Exposition,1998.
    [22] 文莉,刘正士,葛运建. 小波去噪的几种方法[J]. 合肥工业大学学报(自然科学版),2002,25(2): 167-172. WEN Li,LIU Zhengshi,GE Yunjian. Several methods of wavelet denoising[J]. Journal of Hefei University of Technology (Natural Science),2002,25(2): 167-172. (in Chinese

    WEN Li, LIU Zhengshi, GE Yunjian. Several methods of wavelet denoising[J]. Journal of Hefei University of Technology (Natural Science), 2002, 25(2): 167-172. (in Chinese)
    [23] DONOHO D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3): 613-627. doi: 10.1109/18.382009
    [24] JAIN S,CHAUHAN R. Handwritten digit recognition using DNN,CNN and RNN[C]//Advances in computing and data sciences: Second International Conference in Computing and Data Sciences. Dehradun,India: ICACDS,2018: 239-248.
    [25] MALLAT S G. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7): 674-693. doi: 10.1109/34.192463
    [26] 周志华. 机器学习[M]. 北京: 清华大学出版社,2016.
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  • 收稿日期:  2022-06-10

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