Study on Planetary Gear Degradation State Recognition Method Based on the Features With Multiple Perspectives and LLTSA

Abstract

Planetary gear is an important part of the transmission system of large electromechanical equipment. Therefore, it is very important to monitor the degradation of the state of the planetary gear. A method for the degradation state recognition of planetary gear based on the features with multiple perspectives and linear local tangent space alignment (LLTSA) algorithm is presented. First, the time domain features of the original vibration signal are extracted, which have the statistical properties and global significance. Then, the detailed features which pay more attention to the detailed information of the vibration signal are extracted on the basis of improved complete ensemble empirical mode decomposition with adaptive noise, and all those features constitute high dimensional original features. In order to solve the problems of information redundancy and interference features, the original features are processed by LLTSA, and the extraction of low dimensional sensitive features can be achieved. Finally, the optimized support vector machine is studied to recognize the low dimensional sensitive features. The result shows that the proposed method can recognize different degradation states of planetary gear accurately and effectively.