Prediction Of Involute Cylindrical Gear Contact Life Based On Multifractal And Grey Model

Gears are widely used as the transmission parts of mechanical equipment, the stability of mechanical equipment is determined by its running state. In order to ensure the stable operation of mechanical equipment and to determine maintenance actions, gear life prediction has been a hot research topic for engineers and scholars. Traditional gear life prediction methods are mostly based on fatigue cumulative damage theory that requires accurate load spectrum and S-N curve of gear which is difficult to be obtained due to the load change of gears during operation. In addition, not all the S-N curves of gear material are known,and the S-N curve of the known material which is similar to gear material is usually chosen as a reference. Therefore, these methods are very limited for gear online monitoring and life prediction. Based on the advancement of condition monitoring that gear faults final failures can be both reflected in gear vibration signals—a new method is proposed in this paper for gear life prediction based on vibration signature characterization. Multifractal spectrum width that reflects health status of gears is extracted from the vibration signal based on EEMD and multifractal detrended fluctuation analysis(MF-DFA). The corresponding sequence formed by multifractal spectrum width is used to train grey GM(1,1) forecast model, and thus,the prediction of the remaining gear contact fatigue life is implemented. The method has a certain practical value for the residual life prediction, and it is more convenient because the accurate load spectrum and S-N curve is not necessary.Firstly, a meshing vibration model of a single pair of gears is established in this paper, and the vibration mechanism of gear is studied using this model. The effect of gear fault on vibration signal is also researched by the vibration mechanism of gear. As the nonlinear and non-stationary of gear vibration signals and in order to diminish background noise, EEMD is used to preprocess the signal before extracting feature statistical indicators.After the preprocessing procedure, gear vibration signal is further applied with multifractal detrended fluctuation analysis, multifractal spectrum width is extracted as the statistical feature indicator—to show performance degradation process to reflect health status of gears.Finally, gear contact fatigue life tests of involute spur gears were carried on the mechanical closed power flow gear test bench to collect all gear vibration signals at incremental time by intervals. Gear vibration feature statistical index is extracted based on EEMD and multifractal detrended fluctuation analysis, forming a corresponding feature statistical index sequence. Gear contact fatigue residual life is predicted based on Dynamic Grey GM(1,1) prediction model. The experimental results show that the proposed life prediction method in this paper is effective in predicting the remain useful life of involute cylindrical gears,providing a new approache to realizing the online monitoring and life prediction of gear transmission systems.