摘要
由于轧机振动信号受到强烈的噪声干扰,给故障特征的有效识别和准确提取带来很大困难。直接采用频域分析方法诊断早期故障的收效甚微。利用小波分析的“带通滤波”特性,可以将信号按照特定的频段进行分解,分解后的单层重构可以将噪声与可用信号进行成功分离;根据预先设定的阈值对高频分解系数处理后进行全局重构同样可以达到消噪的目的。对于现场采集的轧机振动信号,多种方式的消噪结果表明,含有故障特征的低频信息被成功提取。
Due to strong noise in vibration signals of rolling mills,it is very difficult to identify their fault location and to extract their feature.For incipient faults,the spectrum analysis is not effective.Using the band pass filtering feature of wavelet transformation,the signal can be decomposed in the specific frequency band.Furthermore,the single level reconstruction of the decomposed signals can separate the valuable components from the noise successfully.At the same time,the global reconstruction after processing the high frequency decomposing coefficients can realize the same goal.The signal processing results for the vibration signal of bearings of a rolling mill after using several kinds of denoising technology show that the low frequency information containing fault features is extracted,and the characteristic frequency of incipient faults can be captured from the frequency spectrum of the denoised signal.The proposed method can improve accuracy of fault diagnosis decomposing.
出处
《振动与冲击》
EI
CSCD
北大核心
2007年第5期71-73,103,共4页
Journal of Vibration and Shock
基金
北京工业大学青年科研基金
关键词
轧机
频域分析
小波变换
分解-重构
阈值消噪
rolling mills,frequency domain analysis,wavelet transformation,decomposition-reconstruction,threshold denoising
