Detection of rolling bearing defects using discrete wavelet analysis

In the detection of bearing faults the so much desired objective remains the extraction of the defect vibratory signature from the measured signal in which immerses the random noise and other components of the machine. In this article a denoising method of the measured signals is presented. Based on the optimization of wavelet multiresolution analysis, it uses the kurtosis as an optimization and evaluation criterion, several parameters were then selected. The experimental results show the validity of this method within the detection of several defects simulated on ball bearings. The various configurations, in which the signals were measured, allow leading to optimum conditions of its application. The application of WMRA on filtered signals allows better results than its application on wide bands signals or a simple band pass filtering.     

基于瞬变流和遗传算法的管道泄漏辨识

输水系统管道的泄漏影响瞬变水击波波形的畸变和衰减特性,对泄漏的定位和定量是泄漏辨识研究的重要环节.考虑水力瞬变中非恒定摩阻的影响,将泄漏参数反映在建立的管道瞬变流时域数学模型中,给出离散网格和求解方法,分析不同泄漏参数对瞬变水击波的影响,用遗传算法结合瞬变流时域模型的特征线法最小化管道测点压力实测和计算响应值的均方误差来辨识泄漏参数,其中实测信号首先经过预滤波和小波去噪处理.通过模型试验对瞬变流数学模型和泄漏反问题分析模型作了率定和验证,结果表明此法能有效的辨识出小泄漏参数,并且检测信号主动,阀门扰动可操作性强.