基于迭代广义同步压缩变换的时变工况行星齿轮箱故障诊断

Planetary Gearbox Fault Diagnosis under Time-variant Conditions Based on Iterative Generalized Synchrosqueezing Transform

同步压缩变换在分析频率恒定的单分量信号时改善时频可读性的效果显著,而在分析多分量频率时变信号时存在时频模糊现象,为了解决这一问题,提出迭代广义同步压缩变换方法。通过迭代广义解调分离出各单分量成分,并将时变频率变换为恒定频率。应用同步压缩变换精确估计瞬时频率和时频分布幅值。将各单分量的时频分布叠加获得信号的时频分布。该方法有效改善了同步压缩变换在分析频率时变信号时的时频可读性,并且将其推广应用于多分量信号。应用该方法有效识别了时变工况下行星齿轮箱振动信号的频率组成及其时变特征,准确诊断了齿轮故障。

The synchrosqueezing transform can effectively improve the readability of time-frequency representation of mono-component and constant frequency signals, but blurs still occur to the time-frequency representation when analyzing multi-component and time-variant frequency signals. In order to address the time-frequency blur issue, an improved synchrosqueezing transform based on iterative generalized demodulation is proposed. The signal is decomposed into mono-components of constant frequency, via iterative generalized demodulation and filtering. The instantaneous frequency and time-frequency representation of such mono-component are accurately estimated by synchrosqueezing transform. The time-frequency representation of original signal is obtained by superposing the reconstructed time-frequency representations of all the mono-components. This proposed method effectively improves the time-frequency readability of synehrosqueezing transform when analyzing time-variant frequency signals, and generalizes the synchrosqueezing transform to multi-component signals as well. Using this method, the vibration signal frequency contents and their time-varying characteristics of a wind turbine planetary gearbox under time-variant conditions are effectively identified, and the gear fault is diagnosed.