自适应时变滤波(Adaptive time-varying filtering，ATF)方法是一种基于数据驱动的非平稳信号处理方法，可有效分离变转速下的齿轮故障信号。但在ATF方法中，滤波带宽的选取需依据经验人为选取，具有较大的主观性。因此，针对ATF方法中滤波带宽的自适应优化选取，以齿轮啮合阶次与其临近阶次的能量比作为指标，提出了一种基于带宽优化选取的ATF方法。该方法先采用线调频小波路径追踪算法估计齿轮故障信号中的瞬时频率，然后设置不同的滤波带宽值构建自适应时变滤波器，再对滤波后的信号进行阶次分析，并计算齿轮啮合阶次与其临近阶次的能量比，最后挑选出最佳能量比所对应的滤波带宽作为最优带宽值来对信号进行滤波分析，进而诊断齿轮故障。对变转速下的齿轮局部故障进行了仿真和实例分析，并将之与集合经验模态分解（ensemble empirical mode decomposition，EEMD）方法对比，结果表明，该方法不仅可自适应选取滤波带宽，减少滤波误差，而且能有效提取齿轮故障特征信号，凸显齿轮故障特征。
Abstract
Adaptive time-varying filtering (ATF) method is a non-stationary signal processing method based on data-driven, which can effectively extract gear fault signals under variable rotational speeds. However, in the ATF method, it's subjective that the selection of filtering bandwidth needs to be manually selected based on experience. Therefore, aiming at the adaptive optimal selection of filtering bandwidth in the ATF method, an adaptive time-varying filtering method based on bandwidth optimal selection is proposed, taking the energy ratio of gear meshing order and its adjacent order as an index. The method firstly estimates the instantaneous frequency in the gear fault signal by using chirplet path pursuit, then sets different filter bandwidth values to construct an adaptive time-varying filter, then performs order analysis on the filtered signal, and calculates the energy ratio between the gear meshing order and its adjacent order, finally selects the filter bandwidth corresponding to the optimal energy ratio as the optimal bandwidth value to perform filter analysis on the signal, and further diagnoses gear faults. The simulation and case analysis of gear local fault under variable speed are carried out and compared with ensemble empirical mode decomposition（EEMD） method. The results show that this method can not only adaptively select filter bandwidth and reduce filter error, but also effectively extract gear fault feature signal and highlight gear fault characteristics.
关键词
自适应时变滤波 /
滤波带宽 /
线调频小波路径追踪 /
阶次分析 /
能量比 /
齿轮
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Key words
Adaptive time-varying filtering /
Filter bandwidth /
Chirplet path pursuit /
Order analysis /
Energy ratio /
Gear
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