基于MID算法的组合切片分析在滚动轴承故障诊断中的应用

摘要

As a generalization of spectral correlation density,modulation intensity distribution (MID)can be used to extract amplitude modulations of either discrete signals or random ones satisfactorily.However,the computational effort of modulation intensity factor in MID is so heavy that it can not satisfy the real time request of industrial applications. Here,a new method,combined slice analysis based on MID (C-SMID),was proposed to detect the characteristic frequency of bearing faults.Firstly,the speed variation range of a rolling bearing was used to decide the possible frequency fluctuation range of bearing fault signals.Then,the range of the selectivity factor△f was set and the character slices of MID were calculated out.Finally,the bearing fault type was judged based on the signal energy comparison between different slices.Comparing with the original MID,the new method had a better anti-noise property and a less amount of calculation.In order to verify the feasibility and superiority of the new method,several comparative experiments between it and the common envelope demodulation method were performed with the simulated signals and the measured ones acquired from a rolling bearing fault simulation test platform QPZZ-II.The test results showed that the new method has higher detection precision for bearing faults of inner race,outer race and rolling parts.%调制密度分布（MID）作为谱相关密度的开放性推广，能够很好地解决离散或者随机载波调制信号的检测问题，然而该算法在判定轴承故障前需要大量计算描述矩阵来估计调制密度因子，不能满足工业生产的实时性要求。为此，提出基于MID算法的组合切片分析方法，首先根据转速的波动范围，确定故障特征频率相对于理论值的波动范围，然后给定选择性因数（Δf）的范围并计算信号的MID组合切片带，最后通过切片带之间的能量对比确定轴承故障类型。该分析方法不仅对噪声不敏感，而且有效地减少了计算量，满足了实时性要求。随后，分别采用仿真信号和QPZZ－II系统（滚动轴承故障模拟实验平台）的实测数据，对MID组合切片分析方法进行了实验验证，并与包络解调分析进行了对比。实验结果表明，该方法对滚动轴承外圈、内圈和滚动体故障的检测精度更高。