Application of EWT-FastICA in Vibration Signal Identification for nternal Combustion Engines
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摘要: 内燃机广泛应用于工程、动力等领域，然而内燃机因燃烧和机械运动引起的冲击与振动导致其减振降噪一直是研究的热点，而如何准确识别振源则是减振的前提。本文针对振源盲分离时观测信号不少于源信号数目要求不易满足的问题，利用经验小波变换(Empirical wavelet transform, EWT)结合快速独立成分分析(Fast independent component analysis, FastICA)实现对内燃机振源信号的识别。首先使用时域同步平均法对内燃机缸盖的振动信号进行预处理，然后进行经验小波变换，之后再利用皮尔逊相关系数选择有效经验模态分量作为快速独立成分分析(FastICA)的输入，最终分离结果表明：该方法可以有效地从内燃机缸盖振动信号中识别出燃烧信号和气阀机构开启时的气体冲击信号。Abstract: The internal combustion engine is widely used in engineering, power and other fields. However, due to combustion impact, mechanic vibration and the complexity of its structure, the vibration and noise reduction of internal combustion engines has always been a focus of research, and especially how to accurately identify the vibration source is the premise of vibration reduction. For the problem that the number of observed signals is not less than the number of source signals is not easy to meet in the blind separation of vibration sources, in this paper, the empirical wavelet transform (EWT) and fast independent component analysis (FastICA) are used to identify vibration sources of internal combustion engines. Firstly, the time domain synchronous average technique is used to preprocess vibration signal of the internal combustion engines cylinder head, and then experience the wavelet transform, next the Pearson correlation coefficient is used to choose effective empirical mode component as fast independent component analysis (FastICA) input, and finally the separation results show that this method can effectively identify the combustion gas signal and the valve body opens gas combustion impact signal from the internal combustion engines cylinder head vibration signal.
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[1] 杨甜甜. 基于振动信号时频分析的柴油机缸内燃烧状态监测研究[D]. 太原: 太原理工大学, 2019YANG T T. Investigation into the condition monitoring of in-cylinder combustion behaviors of diesel engines based on time-frequency analysis of vibration signals[D]. Taiyuan: Taiyuan University of Technology, 2019 (in Chinese) [2] 贝绍轶, 赵景波, 雷卫宁, 伟书等. 基于"源-通道-接收体"模型的汽车异常振动故障诊断[J]. 中国机械工程, 2018, 29(4): 384-389BEI S Y, ZHAO J B, LEI W N, et al. Abnormal vibration fault diagnosis of cars based on "source-path-receiver"model[J]. China Mechanical Engineering, 2018, 29(4): 384-389 (in Chinese) [3] 王培良, 叶晓丰, 杨泽宇. 独立成分相关分析的自适应故障监测方法[J]. 控制理论与应用, 2018, 35(9): 1331-1338 https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201809012.htmWANG P L, YE X F, YANG Z Y. Adaptive fault detection method based on correlation analysis of independent component[J]. Control Theory & Applications, 2018, 35(9): 1331-1338 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201809012.htm [4] BI F, LI L, ZHANG J, et al. Source identification of gasoline engine noise based on continuous wavelet transform and EEMD-RobustICA[J]. Applied Acoustics, 2015, 100: 34-42 doi: 10.1016/j.apacoust.2015.07.007 [5] TENGTRAIRAT N, WOO W L. Single-channel separation using underdetermined blind autoregressive model and least absolute deviation[J]. Neurocomputing, 2015, 147: 412-425 doi: 10.1016/j.neucom.2014.06.043 [6] 韦成龙, 周以齐, 李瑞, 于等. 基于改进S变换和ICA的相关源分离方法[J]. 振动、测试与诊断, 2019, 39(4): 852-859, 910WEI C L, ZHOU Y Q, LI R, et al. Blind Separation of correlated sources based on modified S-transform and ICA[J]. Journal of Vibration, Measurement & Diagnosis, 2019, 39(4): 852-859, 910 (in Chinese) [7] 景亚兵, 刘昌文, 毕凤荣. 基于信号分析的发电机组辐射噪声盲源分离和识别[J]. 内燃机工程, 2017, 38(2): 141-145 https://www.cnki.com.cn/Article/CJFDTOTAL-NRJG201702026.htmJING Y B, LIU C W, BI F R. Blind source separation and identification of generator radiation noise based on signal analysis[J]. Chinese Internal Combustion Engine Engineering, 2017, 38(2): 141-145 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NRJG201702026.htm [8] 韩春杨, 姚国凤, 赵建, 修等. 柴油机振动信号盲分离组合算法[J]. 振动与冲击, 2014, 33(6): 44-47, 52HAN C Y, YAO G F, ZHAO J, et al. Combination algorithm for blind separation of diesel engine vibration signal[J]. Journal of Vibration and Shock, 2014, 33(6): 44-47, 52 (in Chinese) [9] YAO J C, XIANG Y, QIAN S C, et al. Noise source separation of diesel engine by combining binaural sound localization method and blind source separation method[J]. Mechanical Systems and Signal Processing, 2017, 96: 303-320 doi: 10.1016/j.ymssp.2017.04.027 [10] 罗德扬. 时域同步平均原理与应用[J]. 振动、测试与诊断, 1999(3): 202-207 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS199903009.htmLUO D Y. Principals and applications of time domain synchronous averaging[J]. Journal of Vibration, Measurement & Diagnosis, 1999(3): 202-207 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS199903009.htm [11] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995 doi: 10.1098/rspa.1998.0193 [12] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010 doi: 10.1109/TSP.2013.2265222 [13] GILLES J, TRAN G, OSHER S. 2D empirical transforms. wavelets, ridgelets, and curvelets revisited[J]. SIAM Journal on Imaging Sciences, 2014, 7(1): 157-186 doi: 10.1137/130923774 [14] GILLES J, HEAL K. A parameterless scale-space approach to find meaningful modes in histograms-Application to image and spectrum segmentation[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2014, 12(6): 1450044 doi: 10.1142/S0219691314500441 [15] 李敏通. 柴油机振动信号特征提取与故障诊断方法研究[D]. 杨凌: 西北农林科技大学, 2012LI M T. Research on diesel enging vibration signal feature extraction and fault diagnosis methods[D]. Yangling: Northwest A & F University, 2012 (in Chinese) [16] PAN J, CHEN J L, ZI Y Y, et al. Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment[J]. Mechanical Systems and Signal Processing, 2016, 72-73: 160-183 doi: 10.1016/j.ymssp.2015.10.017 [17] 王霞, 刘昌文, 毕凤荣, 等. 基于独立分量分析及小波变换的内燃机辐射噪声盲源分离和识别[J]. 内燃机学报, 2012, 30(2): 166-171 https://www.cnki.com.cn/Article/CJFDTOTAL-NRJX201202013.htmWANG X, LIU C W, BI F R, et al. Blind source separation and identification of engine radiation noise based on independent component analysis and wavelet transform[J]. Transactions of CSICE, 2012, 30(2): 166-171 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NRJX201202013.htm -
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