摘要
针对传统齿轮故障信号处理中,对单一信号进行处理造成的敏感特征信息遗失问题,提出形态非抽样小波(MUDW)的多测点齿轮故障振动信号融合预处理方法,以充分利用各信号所蕴含的特征信息,减少噪声及干扰成分的影响。该方法首先应用时域同步平均(TSA)对各通道信号进行预处理;然后,根据形态非抽样小波的基础框架将信号分解,并采用相关峭度(CK)和加权运算的方式,表征各分解层近似信号对故障特征的贡献量,提高有用近似信号的比重;在此基础上,建立融合准则将各分解层信号进行融合,改善重构信号的特征信息;最后,齿轮裂纹故障实验证明:该方法能够较好地抑制噪声,明显突出故障齿轮啮合频率及其倍频,融合效果理想。
Aimed at the problems that in the traditional gear fault signal processing,the sensitive characteristic information is easily lost in the process of the single signal processing,a formal sampling wavelet(MUDW)fusion pretreatment method of multipoint gear fault vibration signal is proposed to make full use of the characteristic information of each signal contained to reduce the influence of noise and interference elements.First of all,each channel signal is preprocessed by using the time synchronous average(TSA),and then the morphological un-decimated wavelet framework signals are decomposed,and each decomposition level approximate signal's contribution to the fault is characterized to raise the proportion of useful approximation signals by adopting the correlated kurtosis(CK)and the weighted operation form.On the basis of this,each decomposition level signal is fused by the established fusion rule,improving the reconstructed signal characteristic information.Finally,the experiment of gear crack fault shows that the method can restrain noise well,and the meshing frequency and its double frequency are prominent.The fusion effect is ideal.
作者
仝蕊
康建设
李宝晨
陈疆萍
TONG Rui;KANG Jianshe;LI Baochen;CHEN Jiangping(Unit 93507,Shijiazhuang 050027,China;Shijiazhuang Campus of PLA Army Engineering University,Shijiazhuang 050003,China;Unit 93601,Datong 037006,Shanxi,China)
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2020年第4期7-14,共8页
Journal of Air Force Engineering University(Natural Science Edition)
关键词
信号预处理
多测点信息融合
形态非抽样小波分解
相关峭度
加权融合
signal preprocessing
muliti-point information fusion
morphological un-decimated wavelet decomposition
correlated kurtosis
weighted fusion
