Year: 2025
Type: article
Abstract: With the advancement of intelligent industrial equipment and the growing demand for system digitalization, parametric modeling of pump operational states has become increasingly important. This is especially true for large pumps, where real-time monitoring remains a major challenge. This paper proposes a pump characteristic curve prediction method based on transfer learning. By leveraging characteristic curve data from small, easily testable source domains for pre-training, the learned features are transferred as initial conditions for training performance models of other pump types. The test results show that neural network models pre-trained with transfer learning achieve faster prediction speeds and lower error rates. Transfer learning also demonstrates strong adaptability to characteristic curve data from various pump categories. Under varying volumes of target data, prediction accuracy improves significantly. Notably, when data are limited, the transfer learning approach achieves a prediction error of 5.2%, compared to 48.1% for direct deep learning modeling. Moreover, the proposed method effectively reduces prediction range beyond the scope of the original data.
Source: Processes
DOI: 10.3390/pr13061682
Authors: Erqinhu Ke, Haibo Xu, Yingyi Ma, Ao Wu, Ruijie Zhao
Institution: Jiangsu University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410774341