CN110909827A - 一种适用于风机叶片声音信号的降噪方法 - Google Patents
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Abstract
本发明提出一种适用于风机叶片声音信号的降噪方法,方法包括:首先建立不同风速尺度下的风噪数据库,并对待降噪的叶片声音信号通过左右声道的特性进行预处理;然后取出与待降噪信号同风速下的风噪信号作为参考信号,并基于皮尔逊相关系数和K_means算法找出待降噪信号的含有噪声的帧与不含噪声的帧;最后针对两类帧信号做不同方式的处理,并进行帧还原,从而得到纯净的风机叶片声音信号。本发明可以对风场中声音信号实现降噪的预处理,滤除长年存在的风噪的干扰。
Description
技术领域
本发明涉及一种适用于风机叶片声音信号的降噪方法,针对风场中采集风机声音时会受到的风噪以及其他随机噪声的干扰问题,设计一种能够消除这些扰噪声的流程方法,属于声音信号降噪技术领域。
背景技术
风电场的叶片故障检测一直是一个不容忽视的问题,如果不能够在叶片故障初期诊断出故障,随着运行时间的增长,故障程度会进一步加深,造成维修成本加大,降低风能的捕获效率,严重时会降低叶片的使用寿命。
国内外众多学者在叶片的该组航诊断中做了大量的研究,而基于风机叶片声音信号实现叶片的故障诊断鲜有研究,在风场中采集风机声音信号时主要受到风噪的影响,这对基于声音信号的故障诊断造成极大的干扰,也是制约这方面研究的关键环节,所以如何消除风噪,得到纯净的风机叶片声音信号对叶片的故障诊断意义深远。
针对风机的降噪问题,普遍的方法是基于滤波器、小波变换、EMD分解等算法实现去噪,不同于这些算法,本发明通过建立不同风速尺度下的风噪信号数据库,从分帧的角度对含噪帧信号进行重新拟合,进而实现降噪的目的。
发明内容
本发明旨在研究一种适用于风机叶片声音信号的降噪方法,针对一个风场,首先建立不同风速尺度下风噪信号数据库,并将需要去噪的声音信号分帧处理并提取各帧的梅尔倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC);然后依据和风噪信号的相关性寻找到含噪帧和非噪帧,并对两种类型的帧信号进行不同的处理;最后将各帧信号重构回去从而得到纯净的风机叶片声音信号,实现声音信号的降噪处理。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1为本发明公开的一种适用于风机叶片声音信号的降噪方法流程图;
具体实施方式
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
如图1所示,为本发明一种适用于风机叶片声音信号的降噪方法流程图,其步骤包括:
步骤1.风场中的风噪具有随机性,并不是一直存在的,所以针对某一风场,在远离风机的场地利用单声道采集装置采集不同风速尺度下的风噪信号,建立风场风噪信息的数据库,其中风速尺度间隔为1m/s;
步骤2.针对需要进行降噪处理的叶片声音信号x1(t)利用双声道声音采集装置进行采集,在时域上分别刻画左声道与右声道的待降噪信号的波形,对于某一个时间点的波形幅值,取对应时间点的左右声道波形幅值的最小值,依此原则,得到基于左右声道预处理后的叶片声音信号x2(t);
步骤3.从风噪数据库中,取出与待降噪信号同风速的参考风噪信号z(t),并分别针对z(t)、x2(t)做相同的分帧处理,其中x2(t)被分为n1帧,z(t)被分为n2帧,分帧公式如下:
fn=(N-wlen+inc)/inc (1)
overlap=wlen-inc (2)
式中,N为声音信号的长度,wlen为设置的帧长,inc为设置的帧移,通常是帧长的1/4左右,overlap为帧重叠,fn为信号分成的帧数;
MFCC是一种数据压缩技术,能够用一组12—16数值组成的向量代表一帧信号的特征情况,提取z(t)与x2(t)的每帧的MFCC系数,即mfcc(z,n2)、mfcc(x2,n1);
步骤4.针对x2(t)的每帧信号的mfcc(x2,k),分别求取与z(t)所有帧mfcc(z,q)的皮尔逊系数,然后求取平均值,从而得到x2(t)的每帧信号与参考风噪的相关性大小R(k),其中k取1—n1,q取1—n2,涉及到的公式如下:
式(3)是求取x,y两个变量皮尔逊相关系数的的过程Cov(x,y)是变量x,y的协方差,σx和σy分别为x,y的方差;
针对R(k)构成的数据集合,采用K_means方法进行聚类,聚类数目为2,聚类结果中,数值较小的类中R(k)对应的是帧是不含噪声的非噪帧,而数值较大的类中R(k)对应的是帧是含有噪声的含噪帧;
步骤5.针对非噪帧不做任何处理,予以保留,对含噪帧而言,任取一帧风噪帧进行傅里叶(FFT)变换,将含噪帧在频域上与风噪帧做差,重新进行频域上谱线的刻画,从而得到含噪帧的处理结果;
步骤6.对处理后的含噪帧和非噪帧进行帧还原处理,取出x2(t)第1帧的1—overlap数据点和第2帧到n1帧每帧中的overlap+1—wlen数据点重新组成一列数据,则这列数据即为降噪处理后的叶片声音信号。
本发明中应用了具体实施例对本发明的原理及实施方式进行了阐述,以上实施例的说明只是用于帮助理解本发明的方法及其核心思想;同时,对于本领域的一般技术人员,依据本发明的思想,在具体实施方式及应用范围上均会有改变之处,综上所述,本说明书内容不应理解为对本发明的限制。
Claims (6)
1.一种适用于风机叶片声音信号的降噪方法,所述的方法包括:
步骤1.建立不同尺度下的风噪声音信号数据库;
步骤2.基于左右声道预处理需要降噪的叶片声音信号;
步骤3.取出与待降噪信号同风速下的风噪信号,然后针对两种信号做同样的分帧处理,并且提取两种信号的MFCC系数;
步骤4.求取待降噪信号每帧数据与风噪帧的相关系数R(k),并利用K_means聚类方法辨识出不含噪声的帧信号与含噪声的帧信号;
步骤5.保留非噪帧信号,对于含噪帧信号,在频域上与风噪帧信号做差处理;
步骤6.将非噪帧信号和含噪帧信号进行帧还原,从而得到纯净的风机叶片的声音信号,实现了声音的降噪处理。
2.如权利要求1步骤2所述基于左右声道预处理需要降噪的叶片声音信号的特征为:采集信号的声因传感器是双声道的,在时域上分别刻画左声道与右声道的待降噪信号的波形,对于某一个时间点的波形幅值,取对应时间点的左右声道波形幅值的最小值,依此原则,得到基于左右声道预处理后的叶片声音信号x2(t)。
4.如权利要求1步骤4所述利用K_means聚类方法辨识出含噪声帧与非噪帧的特征为:针对R(k)构成的数据集合,设置聚类的数目为2,聚类结果中,数值较小的类中R(k)对应的是帧是不含噪声的非噪帧,而数值较大的类中R(k)对应的是帧是含有噪声的含噪帧。
5.如权利要求1步骤5所述含噪帧与非噪帧处理方式特征为:针对非噪帧不做任何处理,予以保留,对含噪帧而言,任取一帧风噪帧进行傅里叶变换,将含噪帧在频域上与风噪帧做差,重新进行频域上谱线的刻画,从而得到含噪帧的处理结果。
6.如权利要求1步骤6所述帧还原处理的特征为:取出x2(t)第1帧的1—overlap数据点和第2帧到n1帧每帧中的overlap+1—wlen数据点重新组成一列数据,则这列数据即为降噪处理后的叶片声音信号。
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CN115547356A (zh) * | 2022-11-25 | 2022-12-30 | 杭州兆华电子股份有限公司 | 一种基于无人机异常声音检测的风噪处理方法及系统 |
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CN115547356A (zh) * | 2022-11-25 | 2022-12-30 | 杭州兆华电子股份有限公司 | 一种基于无人机异常声音检测的风噪处理方法及系统 |
CN115547356B (zh) * | 2022-11-25 | 2023-03-10 | 杭州兆华电子股份有限公司 | 一种基于无人机异常声音检测的风噪处理方法及系统 |
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