机械故障信号的压缩域信源净化与1.5 维谱诊断方法

Source Purification in Compressed Domain and 1.5-dimensionalSpectrum Diagnosis Method for Mechanical Fault Signals

压缩感知技术通过构造满足约束等距性质(restricted isometry property,RIP)的观测矩阵,能够实现数据的有效降维(即压缩测量),但与之相伴的是如何从压缩信号中高质、高效地重构原始信号.为了规避烦琐的重构流程,提出了一种基于压缩域特征辨识的故障诊断方法.在压缩感知的基本框架下,以行阶梯观测矩阵替代主流的高斯随机测量矩阵,实现对原始信号的压缩测量.针对随机噪声对于压缩观测信号的干扰,建立基于最大相关峭度反卷积(maximum correlation kurtosis deconvolution,MCKD)与1.5维谱的微弱故障特征提取方法,即通过MCKD增强压缩信号中的周期冲击成分,剔除传递路径与背景噪声的干扰,进而采用1.5维包络谱提取故障特征频率.结果表明:该方法不但规避了经典压缩感知的复杂重构过程,而且在受到强噪声干扰的条件下,也能获得准确的故障诊断结果.

Compressed sensing can achieve effective dimension-reduction of data (i. e. , compressedmeasurements) by constructing observation matrix that satisfies the restricted isometry property (RIP).However, along with this comes the challenge of reconstructing the original signal from the compressedsignal with high quality and efficient manner. To circumvent the tedious reconstruction process, thispaper proposed a fault diagnosis method based on compressed domain feature identification. Within thebasic framework of compressed sensing, the well-known Gaussian random measurement matrix wasreplaced by a row ladder observation matrix to achieve compressed measurement of the original signal.Aiming at the interference of random noise on the compressed observation signal, a weak fault featureextraction method based on maximum correlation kurtosis deconvolution (MCKD) and 1.5-dimensionalspectrum was established. In which the periodic shock component in the compressed signal was enhancedby MCKD to eliminate the interference of transmission path and background noise, and the 1.5-dimensional envelope spectrum was subsequently used to extract the fault feature frequency. Thesimulation and experimental results show that the proposed method not only circumvents the complexreconstruction process needed by classical compression sensing, but also achieves accurate fault diagnosisresults under conditions of strong noise interference.