基于提升小波变换和Hilbert调制技术的故障识别方法

Fault Recognition Method Based on Lifting Wavelet Transform and Hilbert Modulation Technology

齿轮局部发生故障后,非线性振动信号频谱中齿轮啮合频率及其二、三次谐波附近的边频带均出现显著增长.由于提升小波算法预测和更新原理与故障信号紧密相关,利用提升小波对振动信号进行时频特性分析和信息预处理,通过预测器和更新器的设计取代小波基函数选取过程;随后对蕴含大量故障特征信息的高频细节信号实施Hilbert变换,调制信号的包络谱中彻底剔除常规振动分量仅保留故障信息,该方法可高效识别振动信号频谱中的齿轮故障特征频率.最后用实例验证基于提升小波变换的Hilbert调制分析在齿轮故障诊断中的有效性.

The vibration signals generated by the gear partial failure show non-stationary and non- periodic. The sideband spectrum near the gear mesh frequency, the second and the third harmonics of the corresponding frequency spectrum all grow significantly. Because principles of prediction and update are closely related to the fault information, the vibration signals are first analyzed and preprocessed by lift wavelet, and the wavelet basis function selection is replaced by designing the predictor and updater during the signal decomposition process, which can significantly raise the feature extraction efficiency. The high frequency signal is demodulated by Hilbert transformation, the conventional vibration components are removed but only fault information retained in its envelope spectrum, and the faults are located after the fault feature frequency can be identified effectively. An illustration verifies that the Hilbert modulation technology based on lifting wavelet transform is fully competent for gear fauh diagnosis.