Model-Based Intelligent Non-linear Signal Recognition for Gearbox Condition Monitoring

In this paper, a method for equipment fault diagnosis of gearbox using principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method is to study and monitor the working state of the gearbox by studying the original vibration signal of the gearbox, and establish a corresponding experimental model by using the normal gear and the fault gear, respectively. Firstly, the vibration signal of the gearbox is preprocessed by wavelet packet transform (WPT). Then the time domain signal analysis method is used to extract the characteristic parameters of the vibration signal and the data is reduced by PCA. After the data are reduced in dimension, the principal element with the highest contribution rate are selected as the input parameter of SPRT. Test parameters to verify the proposed SPRT algorithm and Root Mean Square Error (RMSE). The results show that the proposed method is effective and practical.