摘要
在小波熵去噪理论的基础上,对实际隧道爆破工程采集到的爆破振动信号进行了小波熵方法去噪。利用db8小波对去噪后的信号在尺度a=16进行了连续小波变换得到其模极大值,准确识别了隧道多段别微差爆破实际延期时间间隔,并验证了小波熵方法去噪的可靠性。结果表明,小波熵去噪方法能够有效滤除和抑制爆破振动非线性信号所夹杂的高频噪声分量,并且很好地保留了爆破振动信号的突变细节。对滤波后的信号进行模极大值变换,信号局部奇异点的辨识更准确,可以精确识别隧道微差爆破延时间隔。
In this paper,based on the theory of wavelet entropy de-noising,the wavelet entropy method is established for the blasting vibration signals collected in the actual tunnel blasting engineering. The db8 wavelet is used to carry out the continuous wavelet transform to get the modulus maximum of the signal after de-noising,accurately identify the millisecond delay time interval in tunnel blasting,the reliability of wavelet entropy de-noising method is verified. The results show,the wavelet entropy de-noising method can effectively filter and suppress the high frequency noise component of the nonlinear signal of blasting vibration,and the mutation details of blasting vibration signal can be preserved well. After the filtering the signal is carried out modular maximum transform,the signal local singular point identification is more accurate,which can precisely identify the delay interval in the tunnel millisecond blasting.
出处
《工业安全与环保》
北大核心
2016年第12期1-3,共3页
Industrial Safety and Environmental Protection
基金
国家自然科学基金(51274203)
关键词
爆破振动
小波熵去噪
模极大值
微差识别
blasting vibration
wavelet entropy de-noising
modulus maximum
delay time identification