Aiming at the problem of gear fault detection, identification and classification under variable speed and variable load conditions, a new intelligent fault diagnosis method based on maximum overlapping discrete wavelet packet transform and artificial neural network was proposed. The maximum overlapping discrete wavelet packet transform in the autocorrelation spectral kurtosis graph was studied, and the complex gear fault vibration signal was decomposed into frequency bands and central frequencies called nodes. Then, the autocorrelation of the square envelope of each node was derived to calculate the kurtosis of each node at each decomposition level, thus the influence of aperiodic pulse and noise was reduced. The feature matrix obtained in the previous step was used as the input of radial basis function neural network, so as to realize the automatic classification of gear status. At last, the gearboxes with healthy state and five different types of gear faults were tested and analyzed under variable speed and variable load (16 kinds) conditions. The results show that this method can be used to extract the feature information better, locate the appropriate demodulation frequency band for gear fault diagnosis, and the accuracy of gear fault diagnosis under all working conditions is improved.
ZHUANG Min, LI Ge, DING Kexin, XU Guansheng. Intelligent Fault Diagnosis of Gears under Variable Speed and Variable Load Conditions[J]. Machine Tool & Hydraulics,2022,50(12):181-186