基于SVD与Fast Kurtogram算法的滚动轴承声发射故障诊断

Acoustic emission fault diagnosis of rolling bearings based SVD and Fast Kurtogram algorithm

针对声发射检测齿轮箱轴承故障问题,提出基于奇异值分解(Singular Value Decomposition,SVD)与Fast Kurtogram算法的故障诊断方法。通过奇异值分解提高信号信噪比;将Fast Kurtogram算法用于故障信号共振解调带通滤波器参数确定,结合能量算子解调包络谱,成功提取齿轮箱轴承内外圈故障特征,有效改善传统共振解调中人工选择滤波器参数的不确定性。通过仿真与实验数据验证所提方法的有效性。

Aiming at problems of fault diagnosis of rolling bearings of a gearbox using acoustic emission,the method based on SVD and Fast Kurtogram algorithm was proposed.Firstly,background noise was restrained to increase signal-to-noise ratio with SVD,then the best parameters of a band-pass filter for resonance demodulation of fault signals were determined with the Fast Kurtogram algorithm,and the fault features of inner and outer races of a rolling bearing were extracted with energy operator demodulation envelope spectrum to effectively improve uncertainty of artificial selection of filter parameters in traditional resonance demodulation.The results of simulations and testing data showed that the proposed method is effective.