Selective health indicator for bearings ensemble remaining useful life prediction with genetic algorithm and Weibull proportional hazards model

Abstract For mastering device behavior and establishing the mathematical mapping relationship between the degradation process and the operation parameters, we proposed an ensemble RUL prediction model with GA, SVR, and WPHM, and the accuracy and effectiveness of the proposed method were validated by a bearing experiment. To better characterize bearing degradation behavior, a HI construction algorithm was proposed with four metrics of monotonicity, prognosability, trendability and robustness, and RUL prediction was implemented by SVR and WPHM. To verify the superiority of SVR, the performance was compared with NAR-NN, BP-NN, LSTM, GM, ARMA under three criteria including MSE, MAE, and MAPE. Results show that the minimum errors with MSE, MAE, and MAPE appear in SVR, meaning that SVR is the most suitable method in the pseudo operation information prediction. Additionally, the predicted RUL results are basically as same as the actual value by inputting the pseudo operation information into WPHM RUL function.

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