Abstract
Hydraulic pumps, vital in modern industrial equipment, face the challenge of direct flow rate measurement due to their intricate internal structures. Consequently, devising predictive methods for the main pump flow is crucial for early anomaly detection and efficient maintenance. This paper introduces a predictive method for hydraulic pump flow based on Long Short-Term Memory networks (LSTM), known for their robust handling of temporal data. Utilizing LSTM, the method predicts flow rates, which are then employed to compute the volumetric efficiency under steady rotational conditions, thus evaluating the pump’s operational status. The proposed model’s experimental validation, marked by a low mean square error in flow prediction, attests to its efficacy. Moreover, the derived average volumetric efficiency value of 0.97 serves as a reliable indicator for identifying potential anomalies in hydraulic pump performance.
This work was supported in part by the Fundamental Research Fundsfor the Central Universities under Grant 2019ZDPY08.
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Ma, J., Wang, Y., Wen, J., Zhang, B., Li, W. (2024). Early Anomaly Detection in Hydraulic Pumps Based on LSTM Traffic Prediction Model. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 704. Springer, Cham. https://doi.org/10.1007/978-3-031-57919-6_1
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DOI: https://doi.org/10.1007/978-3-031-57919-6_1
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