Predicting the Remaining Life of Centrifugal Pump Bearings Using the KPCA–LSTM Algorithm

Rongsheng Zhu, Xinyu Zhang, Qian Huang, Sihan Li and Qiang Fu ()
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Rongsheng Zhu: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Xinyu Zhang: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Qian Huang: China Nuclear Power Engineering Corporation Limited, Beijing 100840, China
Sihan Li: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Qiang Fu: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China

Energies, 2024, vol. 17, issue 16, 1-16

Abstract: This paper proposes a data-driven prediction scheme for the remaining life of centrifugal pump bearings based on the KPCA–LSTM network. A centrifugal pump bearing fault experiment bench is built to collect data, and the performance of time domain, frequency domain, and time-frequency domain characteristics under different working conditions is analyzed. Time domain characteristics, frequency domain characteristics, wavelet packet decomposition energy characteristics, and CEEMDAN energy features are found to be able to capture fault information under different working conditions. Therefore, 43 sensitive features are determined from the time domain, frequency domain, and time-frequency domain. Through the analysis of XJTU-SY bearing life cycle data and based on the weighted scores of monotonicity, robustness, and trend indicators, twelve outstanding characteristics of the bearing in the whole life cycle are selected, and a one-dimensional feature quantity that can characterize the life-degradation process of the centrifugal pump bearing is constructed after KPCA dimension reduction processing. The LSTM network, sensitive to time series, is selected to predict and analyze the constructed one-dimensional feature trend, and the prediction effects of the BP network and the CNN network are compared. The results show that this method has advantages in prediction accuracy and model training time.

Keywords: centrifugal pump; rolling bearing; life prediction; feature fusion; KPCA; LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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