变转速条件下基于改进重采样算法的滚动轴承故障诊断

摘要

为解决传统重采样算法在滚动轴承故障诊断中计算精度和计算效率方面的问题,提出了一种基于转速脉冲等分间隔的重采样算法.首先,确定每个转速脉冲上升沿的时间坐标及其对应的故障轴承信号幅值.其次,均分每个相邻脉冲间的时间间隔,获取均分时间坐标并利用上述均分时标对故障轴承信号进行插值以获取相应的故障轴承信号幅值.最后,将每个相邻脉冲间的时间点及幅值点按顺序排序,进一步将时间坐标转换成角域坐标从而得到故障轴承的重采样信号.对升速及降速下故障轴承信号的处理结果显示所提算法可以有效地应用于变转速条件下的滚动轴承故障诊断.此外,利用传统的计算阶比分析方法对上述实验信号进行分析,对比结果表明所提算法可在更短的时间内获得精度更高的结果.%In order to address the issues of traditional resampling algorithms involving computational accuracy and efficiency in rolling element bearing fault diagnosis,an equal division impulse-based(EDI-based)resampling algorithm is proposed.First,the time marks of every rising edge of the rotating speed pulse and the corresponding amplitudes of faulty bearing vibration signal are determined.Then,every adjacent the rotating pulse is divided equally,and the time marks in every adjacent rotating speed pulses and the corresponding amplitudes of vibration signal are obtained by the interpolation algorithm.Finally,all the time marks and the corresponding amplitudes of vibration signal are arranged and the time marks are transformed into the angle domain to obtain the resampling signal.Speed-up and speed-down faulty bearing signals are employed to verify the validity of the proposed method,and experimental results show that the proposed method is effective for diagnosing faulty bearings.Furthermore,the traditional order tracking techniques are applied to the experimental bearing signals,and the results show that the proposed method produces higher accurate outcomes in less computation time.