摘要
针对皮带机齿轮箱轴承声信号受混响及其他部件运行噪声干扰严重导致声学诊断困难的问题,分析了声信号组成、混响产生原因、信号传递路径及各干扰成分特点,提出了一种结合卡尔曼滤波与最优子带选取的声信号特征增强方法。首先,依据最大峭度准则设定了卡尔曼滤波超参数,通过卡尔曼滤波减少了混响噪声对轴承故障声信号的干扰;然后,采用小波包降噪算法对去混响后的信号进行了处理,对比了待测状态与正常状态信号子带的能量差异,选取了包含故障信息多的最优子带,通过包络谱分析提取了轴承故障特征;最后,通过轴承故障模拟试验对基于卡尔曼滤波与小波包子带选取方法的有效性进行了验证,并将其与改进奇异值分解(ISVD)与共振稀疏分解(RSSD)结合的方法进行了比较。研究结果表明:该方法的去混响和降噪效果显著,包络谱中含有明显的故障频率及其相关成分。采用基于卡尔曼滤波与子带选取的方法可以实现在室内测量环境下的轴承声信号增强目的,准确提取轴承故障特征。
Aiming at the problem that the sound signal of gearbox bearing of belt conveyor was seriously disturbed by reverberation and running noise of other components,which led to the difficulty of acoustic diagnosis,the composition of acoustic signal,the cause of reverberation,the signal transmission path and the characteristics of each interference component were analyzed,and an acoustic signal feature enhancement method combining Kalman filter and optimal sub-band selection was proposed.Firstly,the Kalman filter hyperparameters were set according to the maximum kurtosis criterion,and Kalman filtering was used to reduce the interference of the reverberant noise to the fault acoustic signal of the bearing.Then,the wavelet packet noise reduction algorithm was used to process the signal after de-reverberation,the energy difference was compared between the sub-bands of the signal in the state to be measured and the normal state,the optimal sub-band containing the most fault information was selected,and the bearing fault features were extracted by the envelope spectrum analysis.Finally,the effectiveness of the proposed method was verified by bearing fault simulation tests and compared with the combination of improving singular value decomposition(ISVD)and resonance-based sparse signal decomposition(RSSD).The results of the study show that the proposed method is effective in de-reverberation and noise reduction,and the envelope spectrum contains obvious fault features and their associated components.The proposed method can realize the enhancement of bearing acoustic signal in indoor measurement environment and accurately extract the bearing fault characteristics.
作者
杨小权
刘曰木
刘江
YANG Xiaoquan;LIU Yuemu;LIU Jiang(CHN ENERGY Yulin Energy Co.,Ltd.,Yulin 719000,China)
出处
《机电工程》
CAS
北大核心
2023年第11期1673-1681,共9页
Journal of Mechanical & Electrical Engineering
基金
青龙寺煤矿智能矿山建设关键技术研究项目(GJNY-22-132)。
关键词
皮带输送机
齿轮箱轴承
混响效应
声学诊断技术
卡尔曼滤波
小波包分解
声信号
belt conveyor
gearbox bearing
reverberation effect
acoustical-based diagnosis(ABD)technology
Kalman filter
wavelet packet decomposition
acoustic signal
