【摘要】 振动信号分析是轴承故障诊断中的重要技术手段之一。变转速工况下的滚动轴承振动信号是典型的非平稳信号，并且在转频变化较小的工况中还存在噪声干扰的问题，使传统的时频分析技术难以应用。为解决该问题，提出了一种基于经验最优包络(empirical optimal envelope, EOE)的局部均值分解(local mean decomposition, LMD)和采用分段线性插值的计算阶次跟踪(computing order tracking, COT)算法相结合的故障诊断方法。首先，确定低通滤波器的截止频率和滤波阶数，对滚动轴承振动信号进行滤波，并对滤波后的包络信号进行COT,以获得角域平稳信号。然后，利用EOE＿LMD对重采样后的平稳信号进行处理，得到若干乘积函数(product function, PF)分量。最后，通过计算各分量的信息熵和相关系数，选取合适的分量进行阶次分析，以判断变转速滚动轴承的故障类型。结果表明，该方法可以消除转速波动对故障特征提取的影响，在不同转速变化条件下对滚动轴承具有良好的故障诊断能力。更多还原

【Abstract】 Vibration signal analysis is one of important technical means in bearing fault diagnosis. Vibration signals of rolling bearing under variable speed condition are typical non-stationary signals. There is noise interference in conditions with smaller rotating speed fluctuations to make traditional time-frequency analysis techniques be difficult to apply. Here, to solve this problem, a fault diagnosis method combining local mean decomposition(LMD) based on empirical optimal envelope(EOE) and computing order tracking(COT) algorithm using piecewise linear interpolation was proposed. Firstly, cutoff frequency and filtering order of low-pass filter were determined to filter vibration signal of rolling bearing, and COT was performed for the filtered envelope signal to obtain a stationary signal in angular domain. Then, EOE＿ LMD was used to process the resampled stationary signal, and obtain several product function(PF) components. Finally, by calculating information entropies and correlation coefficients of various components, appropriate components were chosen for order analysis to judge fault types in variable rotating speed rolling bearing. The results showed that the proposed method can eliminate effects of rotating speed fluctuations on fault feature extraction, and have good fault diagnosis ability for rolling bearing under conditions of different rotating speed variations.更多还原