自适应提升多小波在螺旋伞齿轮故障诊断中的应用

Application of adaptive multiwavelets via lifting scheme in bevel gear fault diagnosis

螺旋伞齿轮能改变动力的传递方向,工程上得到了广泛的应用,因此对其进行故障诊断具有重要的意义;然而,重合度大和调幅调频的特性大大增加了特征提取的难度。多小波具有多重小波基函数和许多优良特性,近来被广泛运用于旋转机械的故障诊断。首先用对称提升构造出自适应的多小波,并对信号进行分解;其次,选择敏感特征频带进行重构;最后,通过希尔伯特变换解调出特征频率。以实验台中模拟的螺旋伞齿轮断齿和擦伤故障为例,验证了该方法的有效性。

Different from parallel-axes gears, the bevel gears can change the direction of power transmission. So they are widely used in transmission systems, and their fault diagnosis is of great significance. However, the feature extrac- tion for the faulty spiral bevel gears is quite difficult because of its large overlap ratio as well as amplitude and fre- quency modulation （AMFM） nature. Therefore, it is urgent to develop an effective feature extraction method for this task. Multiwavelet with multiple wavelet basis functions and many excellent properties provides an effective tool to ro- tating machinery fault diagnosis. In this paper, firstly, we construct an adaptive muhiwavelet via symmetric lifting scheme, and then decompose the original signal;secondly, the signal is reconstructed with chosen sensitive feature bands;thirdly, the reconstructed signal is demodulated to extract the characteristic frequency based on Hilbert trans- form. The spiral bevel gear breakage and scrape faults simulated on the test bench are taken as examples to verify the effectiveness and reliability of the proposed method.