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Health Status Evaluation of Axial Piston Pump Based on Convolutional Neural Network and Long Short-term Memory Network
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Na-sha WEI, Jiang-feng LIU, Ze-peng DING, Zhi-yi TIAN
Science Technology and Engineering | 2025, 25(21) : 8889 - 8897
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Science Technology and Engineering | 2025, 25(21): 8889-8897
• Papers·Mechanical and Instrumental Industry •
Health Status Evaluation of Axial Piston Pump Based on Convolutional Neural Network and Long Short-term Memory Network
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Na-sha WEI, Jiang-feng LIU, Ze-peng DING, Zhi-yi TIAN
Affiliations
  • School of Vehicle and Traffic Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Published: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2407579
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The plunger pump is one of the important power conversion components of the hydraulic system, and its performance directly affects the safety and stability of the hydraulic system. In order to accurately evaluate the operating status of the plunger pump, a plunger pump health status assessment method based on a combination of convolutional neural network(CNN) and long short-term memory network(LSTM) was proposed, and a genetic algorithm was introduced to optimize the parameters of the neural network. The vibration signals of the plunger pump at different operating moments were collected. The energy characteristics of the vibration signals were extracted by using wavelet packets. Combined with the time-frequency domain characteristics of the signals, a dataset of the health status characteristics of the plunger pump was constructed. The health status was identified and classified by the CNN-LSTM method, and the classification results were evaluated by sample entropy. To verify the effectiveness of this health assessment method, it was applied to the experimental test of the plunger pump. The results show that the recognition accuracy of this method reaches 99%, which can effectively improve the accuracy of the health status assessment of the plunger pump.

axial piston pump  /  convolutional neural network  /  long short-term memory network  /  health assessment
Na-sha WEI, Jiang-feng LIU, Ze-peng DING, Zhi-yi TIAN. Health Status Evaluation of Axial Piston Pump Based on Convolutional Neural Network and Long Short-term Memory Network[J]. Science Technology and Engineering, 2025 , 25 (21) : 8889 -8897 . DOI: 10.12404/j.issn.1671-1815.2407579
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Year 2025 volume 25 Issue 21
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doi: 10.12404/j.issn.1671-1815.2407579
  • Receive Date：2024-10-12
  • Online Date：2026-01-13
  • Published：2025-07-28
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  • Received：2024-10-12
  • Revised：2025-04-16
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    School of Vehicle and Traffic Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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