Prediction of Service Life of Gyro Motor Bearing with Small Sample and Unequally Spaced Data
The bearing vibration signals are difficult to be collectedwhen the existing research results of bearing life prediction are directly applied to gyro motor bearings,and the modeling accuracy is low when the model uses small samples and unequally spaced data.The current signal of gyro motor is selected as the measurable signal,and an implementation standard is formulated to intercept the effective electric signal.Initializing root mean square(IRMS)and Renyi entropy are extracted as degradation features to describe the bearing life.The designed EMD-BBO-GM(1,1)model consists of interval change module,data decomposition module,model construction module and parameter optimization module,which can realize the function of bearing life prediction.A small and micro flexible gyro motoris selected for the bearing life prediction test.The results show that the predicted life of the model is equivalent to the actual life,the fitting accuracy is not less than 98％,and the prediction accuracy is not less than 95％.Compared with the standard GM(1,1)model,the prediction accuracy of this model is improved by 24.975％,and the contributions of interval change module,data decomposition module and parameter optimization module are 90.94％,3.64％and 5.42％,respectively.
gyro motor bearinglife predictioncurrent signalgrey modelunequally spaced data
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