起动工况驾驶性评价指标特征点识别方法
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摘要
针对车辆起动过程的驾驶性评价指标特征点会因发动机转速波形特异性强和外界干扰而造成识别不准确的问题,本文中提出了一种从信号预处理到特征点识别的一套方法。根据驾驶性评价指标和转速曲线时域特征确定了特征点;结合形态滤波-经验模态组合方法对转速信号进行滤波处理;将D-S证据理论与句法模式相结合应用到特征点的识别中。实验结果表明,该方法能有效地识别起动工况发动机转速曲线的评价指标特征点,为后续驾驶性评价指标的获得提供了客观依据。
Aiming at the problem that the feature points of drivability evaluation indicators in vehicle start process are inaccurately identified due to the strong specificity of engine speed waveform and external interference, a set of methods from signal preprocessing to feature point identification are proposed in this paper. The feature points are determined according to drivability evaluation indicators and the time domain features of rotational speed curve. Morphological filtering combined with empirical modal decomposition is adopted to conduct filtering processing on rotational speed signals, while the combination of D-S evidence theory and syntactic pattern is applied to feature point identification. The results of experiment show that the method proposed can effectively identify the feature points of evaluation indicators on engine speed curve in starting condition, providing an objective basis for obtaining drivability evaluation indicators later on.
Aiming at the problem that the feature points of drivability evaluation indicators in vehicle start process are inaccurately identified due to the strong specificity of engine speed waveform and external interference, a set of methods from signal preprocessing to feature point identification are proposed in this paper. The feature points are determined according to drivability evaluation indicators and the time domain features of rotational speed curve. Morphological filtering combined with empirical modal decomposition is adopted to conduct filtering processing on rotational speed signals, while the combination of D-S evidence theory and syntactic pattern is applied to feature point identification. The results of experiment show that the method proposed can effectively identify the feature points of evaluation indicators on engine speed curve in starting condition, providing an objective basis for obtaining drivability evaluation indicators later on.
引文
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[22] ZHANG L J, XU J W, YANG J H, et al. Multiscale mor phology analysis and its application to fault diagnosis[J]. Mechanical Systems & Signal Processing,2008,22(3):597-610.
[23] 吴小涛,杨锰,袁晓辉,等.基于峭度准则EMD及改进形态滤波方法的轴承故障诊断[J].振动与冲击,2015,34(2):38-44.
[24] LI Y F, ZUO M, LIN J H, et al. Fault detection method for railway wheel flat using an adaptive multiscale morpho-logical filter[J]. Mechanical Systems and Signal Processing,2017,84:642-658.
[25] 潘章达,张铖.利用经验模态分解方法消除白噪声及谐波[J].现代电力,2010,27(5):53-56.
[26] 傅京孙.模式识别及其应用[M].北京:科学出版社,1983:302-310.
[27] HUANG K Y. Syntactic pattern recognition[M]. Handbook of Pattern Recognition and Computer Vision,2015:61-103.
[28] JAMES I. A mathematical theory of evidence[J]. Techni-metrics,1976,20(1):242.
[29] 汽车起动性能试验方法:GB/T 12535—2007[S].2007.
[2] BAUSTIAN J, WOLF L. Cold-start/warm-up vehicle perfor mance and driveability index for gasolines containing isobutanol[J]. SAE International Journal of Fuels & Lubricants,2012,5(3):1300-1309.
[3] 宗孔德.多抽样率信号处理[M].北京:清华大学出版社,1996:235-249.
[4] 孟娜,周以齐,曲娟,等.截面线特征直接提取的离散曲率法[J].中国矿业大学学报,2009,38(4):571-575.
[5] 郭学卫,申永军,杨绍普.基于样本熵和分数阶傅里叶变换的滚动轴承故障特征提取[J].振动与冲击,2017,36(18):65-69.
[6] 刘政琨.基于改进EMD的薄板粘接缺陷特征提取方法研究[D].呼和浩特:内蒙古大学,2016.
[7] 赵利强,王建林,于涛.基于改进EMD的输油管道泄漏信号特征提取方法研究[J].仪器仪表学报,2013,34(12):2696-2702.
[8] 张志刚,石晓辉,施全,等.基于改进EMD和谱峭度法滚动轴承故障特征提取[J].振动.测试与诊断,2013,33(3):478-482.
[9] 张建伟,李孟良,艾国,等.车辆行驶工况与特征的研究[J].汽车工程,2005,27(2):220-224.
[10] 胡大盟.图像轮廓的特征描述及其单目标识别方法研究[D].苏州:苏州大学,2016.
[11] 顾步云,周来水,李涛.一种新的截面轮廓特征点识别与分段曲线类型判别算法[J].机械科学与技术,2007,26(11):1398-1402.
[12] 秦磊,胡琼,黄庆明,等.基于特征点轨迹的动作识别[J].计算机学报,2014,37(6):1281-1288.
[13] NGUYEN V, VU N S, GOSSELIN P H. A scattering trans form combination with local binary pattern for texture classification[C]. International Workshop on Content-Based Multimedia Indexing. IEEE,2016:1-4.
[14] KUMAR M, DHAR D, CHAKRABARTI M, et al. Optimized cooling for vehicle with start-stop technology: US 8745997[P].2014.
[15] WHITTAL I. Off-cycle fuel consumption evaluation of stop-start systems[J]. Geburtshilfe und Frauenheilkunde,2012,27(3):316.
[16] CHANG W S, KIM H K, KIM M K, et al. Development of an evaluation method for quantitative drivability in heavy-duty vehicles[J]. Journal of Mechanical Science & Technology,2014,28(5):1615-1621.
[17] HAYAT O, LEBRUN M, DOMINGUES E. Powertrain driveability evaluation: analysis and simplification of dynamic models[C]. SAE Paper 2003-01-1328.
[18] 王文波,张晓东,汪祥莉.基于独立成分分析和经验模态分解的混沌信号降噪[J].物理学报,2013,62(5):27-34.
[19] TANDON N. A comparison of some vibration parameters for the condition monitoring of rolling element bearings[J]. Measurement,1994,12(3):285-289.
[20] 张文斌,杨辰龙,周晓军.形态滤波方法在振动信号降噪中的应用[J].浙江大学学报:工学版,2009,43(11):2096-2099.
[21] 刘文海,李关防.经验模态分解在直升机谱特征提取中的应用[J].哈尔滨工程大学学报,2010,31(6):714-719.
[22] ZHANG L J, XU J W, YANG J H, et al. Multiscale mor phology analysis and its application to fault diagnosis[J]. Mechanical Systems & Signal Processing,2008,22(3):597-610.
[23] 吴小涛,杨锰,袁晓辉,等.基于峭度准则EMD及改进形态滤波方法的轴承故障诊断[J].振动与冲击,2015,34(2):38-44.
[24] LI Y F, ZUO M, LIN J H, et al. Fault detection method for railway wheel flat using an adaptive multiscale morpho-logical filter[J]. Mechanical Systems and Signal Processing,2017,84:642-658.
[25] 潘章达,张铖.利用经验模态分解方法消除白噪声及谐波[J].现代电力,2010,27(5):53-56.
[26] 傅京孙.模式识别及其应用[M].北京:科学出版社,1983:302-310.
[27] HUANG K Y. Syntactic pattern recognition[M]. Handbook of Pattern Recognition and Computer Vision,2015:61-103.
[28] JAMES I. A mathematical theory of evidence[J]. Techni-metrics,1976,20(1):242.
[29] 汽车起动性能试验方法:GB/T 12535—2007[S].2007.