基于听觉模型和极值点概率密度的断齿故障特征提取方法研究

PDF(1802 KB)

振动与冲击 ›› 2016, Vol. 35 ›› Issue (19) : 101-106.

论文

吴文寿，李允公，王波，李国萌，石悦红

作者信息 +

A method extracting feature of gear fracture fault based on an auditory model and probability density of extreme points

WU Wen-shou，LI Yun-gong，WANG Bo，LI Guo-meng，SHI Yue-hong

Author information +

文章历史 +

摘要

齿轮断齿故障的重要特征是啮合过程中在断齿处产生碰撞与冲击。考虑到人耳听觉系统对于突发的瞬态声信号具有本能的反应，为提取断齿故障诱发的瞬态冲击响应成分，提出一种基于听觉模型和信号极值点概率密度的特征提取方法。该方法首先对信号进行GT带通滤波、相位调整及极值点提取，然后计算各极值点的幅值概率密度，通过对其求导判断各滤波通道中是否存在瞬态冲击成分，继而提取与之相关的极值点。同时，由于系统振动时会产生与断齿冲击无关的极值点，为准确提取断齿冲击，根据瞬态信号频带连续性和多频段分布特点，设计了相应的提取方法。经实测信号验证表明，所提方法能准确刻画及提取断齿故障特征，可以在含有多种类型的瞬态冲击响应成分中提取出只由断齿故障所诱发的冲击成分，且提取结果精确度较高。

Abstract

The collision and impact of the gear fracture fault is the most important features in meshing process. Considering the human auditory system has a instinctive reaction for sudden transient acoustic signals, to extract transient impulse response component induced by gear fracture fault, A method extracting feature of gear fracture fault based on auditory model and signal probability density of extreme points is proposed. This method firstly to conduct band-pass filtering with Gammatone filters , phase adjustment and extreme points extraction for signals, and then calculate the amplitude probability density of extreme points ,judging whether there is a transient impact composition by its derivation in filtered signals, then those extreme points that might be associated with transient impact composition were extracted. Meanwhile, due to the system vibration , it will produce extreme points which are irrelevant to impact of the gear fracture, in order to accurately extract the impact of the gear fracture, according to the transient signal frequency band continuity and multi-band distribution characteristics, design appropriate extraction method. It has been verified by the measured signals show that the proposed method can accurately depict and extract feature of gear fracture fault, and can extract impact component which only induced by gear fracture fault in a variety of types of transient impulse response component , and accuracy of extraction results is well.

导出引用

吴文寿，李允公，王波，李国萌，石悦红. 基于听觉模型和极值点概率密度的断齿故障特征提取方法研究[J]. 振动与冲击, 2016, 35(19): 101-106

WU Wen-shou，LI Yun-gong，WANG Bo，LI Guo-meng，SHI Yue-hong. A method extracting feature of gear fracture fault based on an auditory model and probability density of extreme points[J]. Journal of Vibration and Shock, 2016, 35(19): 101-106

参考文献

[1] 唐贵基，庞尔军，王晓龙. 基于EMD的齿轮箱齿轮故诊断          的研究[J]. 机床与液压，2013，41(13)：188-190.
   TANG Gui-ji，PANG Er-jun，WANG Xiao-long. Research on Gear Fault Diagnosis Based on EMD[J]. Mchine Tool ＆ Hydraulics，2013，41(13)：188-190.
[2] 冯伟. 基于振动分析的齿轮断齿故障研究[J]. 广州航海   高等专科学校学报，2007，15(1)：13-16.
   FENG Wei. Investigation on gear fracture failure based on vibration analysis[J]. Journal of Guang Zhou Maritime College，2007，15(1)：13-16.
[3] 马锐，陈予恕. 齿轮传动系统断齿故障的机理研究[J]. 振动与冲击，2013，32(21)：47-51.
   MA Rui，CHEN Yu-shu. Fault mechanism of a gear system with tooth broken[J]. Journal of Vibration and Shock，2013，32(21)：47-51.
[4] Jena D P，Sahoo S，Panigrahi S N. Gear fault diagnosis using active noise cancellation and adaptive wavelet transform[J]. Measurement，2014，47:356-372.
[5] 栗茂林，梁霖，王孙安，等. 基于连续小波系数非线性流形学习的冲击特征提取方法[J].振动与冲击，2012，31(1)：106-111.
   LI Mao-lin，LIANG Lin，WANG Sun-an，et al. Mechanical impact feature extraction method based on nonlinear manifold learning of continuous wavelet coefficients[J]. Journal of Vibration and Shock，2012，31(1)：106-111.
[6] Shibin Wang，Gaigai Cai，Zhongkui Zhu，et al. Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines [J]. Mechanical Systems and Signal Processing，2015，54(55)：16-40.
[7] 严保康，周凤星. 一种基于形态提升的自适应轴承微冲击提取方法[J]. 振动与冲击，2013，32(24)：198-203.
   YAN Bao-kang，ZHOU Feng-xing. Adaptive weak impulse extraction method of rolling bearings based on morphological lifting wavelet[J]. Journal of Vibration and Shock，2013，32(24)：198-203.
[8] Guo W，Tse P W，Djordjevich A. Faulty bearing singnal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition [J]. Measurement，2012，45:1308-1322.
[9] Kaya E M，Elhilali M. A temporal saliency map for modeling auditoryattention [C]//46th Annual Conference on Information Sciences and Systems. Princeton：IEEE，2012:1-6.
[10] 李允公，张金萍，戴丽. 基于极值点概率密度和听觉模型的瞬态信号提取方法研究[J]. 振动与冲击，2015，34(21)：37-53.
Li Yun-gong，Zhang Jin-ping，Dai Li. A method extracting transient signals based on probability density of extreme points and on auditory model[J]. Journal of Vibration and Shock，2015，34(21)：37-53.
[11] Li Yun-gong，Zhang Jin-ping，Dai Li，et al. Auditory-model-
   based feature extraction method for mechanical faults   diagnosis[J]. Chinese Journal of Mechanical Engineering， 2010，21(3)：391-397.