TT变换结合计算阶比跟踪的滚动轴承时变微弱故障特征提取

Time Varying Weak Fault Features Extraction of Rolling Bearings Based on TT Transform and Computed Order Tracking

为解决变转速工况滚动轴承微弱故障特征难以提取的问题,提出了时时(time-time,TT)变换结合计算阶比跟踪(computed order tracking,COT)的滚动轴承时变微弱故障特征提取方法。首先对变转速状态的轴承微弱故障信号进行时时变换,得到反映故障信号二维时时特征的TT变换矩阵。为消除TT变换矩阵的冗余性,提出了基于峭度准则的奇异值分解(singular value decomposition,SVD)方法对TT变换矩阵降噪。然后对降噪后的TT变换矩阵实施TT反变换,获取滚动轴承时变故障特征增强信号。最后对增强信号进行COT分析得到其包络阶比谱,从而提取滚动轴承的故障特征阶次。对仿真信号和实验测试信号进行分析验证,均实现了滚动轴承变转速工况故障类型的精确识别,分析效果优于包络阶比谱方法,证明了该方法的有效性。

In order to solve the problem that weak fault features of rolling bearings under variable speed condition are difficult to extract, a time varying weak fault features extraction method for rolling beatings was proposed based on time-time （TT） transform and computed order tracking （COT）. Firstly, the vibration signal of rolling bearings in variable speed status was proceeded by TT transform to get the TT transform matrix which can reflect the fault signal＇s 2 dimensional time-time characteristics. In order to eliminate the redundancy of TT transform matrix, a novel singular value decomposition （SVD） based on kurtosis criterion was proposed to reduce the noise of TT transform matrix. Then TT reverse transform was performed on de-noised TT transform matrix to obtain the enhanced time varying feature signal of rolling beating. Finally, the enhanced signal was analyzed by COT to get the envelope order spectrum, and the fault characteristic order of rolling beating was extracted from this spectrum. Both simulated and an experimental signals were analyzed, all realized the precise identification of rolling beating fault type under variable speed conditions, and the results are better than envelope order spectrum, which validates the effectiveness of the proposed method.