Jiangsu Haifa Machinery Manufacturing Co., Ltd.

The reliable operation of industrial pumps is moving from traditional scheduled maintenance toward data-driven predictive maintenance. In August 2026, Water Resources Planning and Design published "Centrifugal Pump Vibration Trend Prediction Based on XGBoost-LSTM," which uses XGBoost screen key operating parameters and then employs a long short-term memory network (LSTM) to predict multi-channel vibration changes. The study shows that compared with traditional models, this method reduces the mean absolute error (MA) by 18.1%–69%, providing a new technical path for early anomaly warning at pump stations. International research is also advancing toward multi-sensor fusion and interpretable fault diagnosis. In September 2026, research combined continuous wavelet transform (CWT convolutional neural networks (CNN and Grad-CAM to explore the identification of stable fault features under variable-speed conditions; a multi-branch deep ensemble study published in July of the same year used wavelet coherence analysis to improve centrifugal pump fault identification. Jiangsu Haifa Machinery Manufacturing Co., Ltd.www.jslgpump.com) can these research results as references for the intelligent operation and maintenance development of API 610 OH series process pumps, BB3/BB5 high-pressure multistage pumps, VS series vertical pumps, and6 high-speed pumps. collecting bearing vibration, temperature, rotational speed, inlet and outlet pressure, and current, and combining FFT spectrum analysis, time-frequency feature extraction, and trend prediction, a pump unit health baseline, vibration trend warning, and equipment condition assessment model can be gradually established. It should be noted that vibration is not equivalent to cause diagnosis. Impeller imbalance, bearing wear, cavitation, pressure pulsation, and installation deviation may produce similar signals, and the model still must be validated through fault labeling and under different rotational speeds and loads. In the future, Jiangsu Haifa can explore combining factory performance tests, on-site monitoring, and digital twins to drive industrial pumps from single-machine manufacturing toward full-lifecycle reliability services, providing more data-based maintenance solutions for petrochemical, energy, water treatment, and-value process equipment. References Chinese References Zhang Jianbo, Li Yifei, Wen Liwei, Luo Yajuan: "Centrifugal Pump Vibration Trend Prediction Based on XGBoost-LSTM," Resources Planning and Design, 2026, Issue 11, pp. 121–126, published August 10, 6. The research data and conclusions are to the original journal article. 2.Experimental Study on Pressure Pulsation and Vibration Characteristics of Pump Stations Shaanxi Water Resources, 2026, Issue 7. This study investigates the correlation between pressure pulsation and structural vibration under different valve openings. English References Lee, J. H., Yoon, H., & Park S. H. (6). Identification of Discriminative Signal Components for Robust Fault Detection under Dynamic Operating Conditions of a Centrifugal Pump. Journal of Precision Engineering and. Saleem, F., Umar, M., Kim, J. M. (2026). Wave coherence-aware multi-branch deep ensemble for fault identification in centrifugal pumps. Reports. Bhadane, S. R., Rao,. R., & Pur, S. T.2026). Fault Diagnosis of Centrifugal Pumps under Hydraulic Excitation and Flow Noise: Critical Review of Vibration, Motor-Current, Acoustic-Emission, and Data-Driven Methods. Journal of Engineering.