Characteristic analysis and life prediction of centrifugal pump bearing based on data drive
Centrifugal pumps are essential equipment for energy conversion and fluid transfer in the industry,and the reliability of their component rolling bearings is crucial for the safe operation of the entire unit.To address the current challenge of predicting the life of rolling bearings,a study was conducted to determine the best prediction method for the remaining life of rolling bearings.Firstly,the performance differences of characteristics in the time domain,frequency domain,and time-frequency domain under various working conditions were analysed.The analysis data collected from a test bench under normal and fault conditions of centrifugal pump bearings were utilized.It was found that the fault information under different working conditions was captured by time domain characteristics,frequency domain characteristics,wavelet packet decomposition energy characteristics,and fully adaptive noise complete ensemble empirical mode decomposition(CEEMDAN)energy characteristics.Subsequently,based on the weighted scores of monotonicity and trend indicators,along with the sensitivity analysis results of the features,12 features with outstanding performance throughout the bearing's life cycle were optimized.After dimensionality reduction treatment using kernel principal component analysis(KPCA)-long short-term memory network(LSTM),one-dimensional characteristic quantities were constructed to characterize the degradation process of centrifugal pump bearings.Finally,the prediction effects of the LSTM network,backpropagation(BP)network,and convolutional neural network(CNN)network were compared and analysed.The research results indicate that the root mean square error(RMSE)of the LSTM network is0.402,and the mean absolute percentage error(MAPE)is 0.332,showcasing the best prediction accuracy among the three models.Additionally,the model average training time is 12.6 s,further demonstrating the LSTM network's advantages in prediction accuracy and on model training time.
vane pumprolling bearingfully adaptive noise complete ensemble empirical mode decomposition(CEEMDAN)kernel principal component analysis(KPCA)long short-term memory network(LSTM)bearing degradation process
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