论文部分内容阅读
针对滑动轴承状态监测时声发射信号的噪声干扰严重、突发性强和信号处理量大的特点,采用形态滤波对声发射信号进行降噪处理.对形态滤波器进行了优化设计,针对某310MW汽轮发电机组滑动轴承现场试验获得的声发射信号进行了实时形态滤波,在此基础上实时计算了4号滑动轴承升速过程中声发射信号的时域特征参数(均方根值VRMS、峰值Vc、峭度因子Fk),并与小波滤波法的计算值进行了对比.结果表明:经优选合适的结构元素设计的形态滤波器能更好地滤除滑动轴承声发射信号的噪声、保留原始信号特征,滤波效果优于小波滤波方法;经形态滤波后的实时特征参数能快速准确地诊断滑动轴承润滑故障,在滑动轴承声发射状态监测中具有很好的工程应用价值.
Aiming at the serious noise disturbance, strong suddenness and large signal processing capacity of acoustic emission signals in the condition monitoring of sliding bearing, the morphological filtering is adopted to reduce the noise of the acoustic emission signal.The morphological filter is optimized and designed for a certain 310 MW The real-time morphological filtering of acoustic emission signals obtained from the field tests of the sliding bearing of the steam turbine generator set was performed. On the basis of this, real-time calculation of the time-domain characteristic parameters of the acoustic emission signal during the process of increasing the speed of the No. 4 plain bearing (root mean square value VRMS, peak value Vc and kurtosis factor Fk) were calculated and compared with the results of wavelet filter.The results show that the morphological filter designed by the proper structural elements can better filter out the noise of the acoustic emission signal of the sliding bearing, and retain the original Signal characteristics and filtering effect are better than those of wavelet filter. The real-time characteristic parameters after morphological filtering can diagnose the lubrication faults of the plain bearing quickly and accurately and have good engineering application value in the acoustic emission state monitoring of the sliding bearing.