摘要
柱塞泵是液压系统重要的动力转换部件之一,其性能好坏直接影响液压系统的安全和稳定。为准确对柱塞泵的运行状态进行评估,提出了一种基于卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory network,LSTM)结合的柱塞泵健康状态评估方法,引入遗传算法对神经网络的参数进行优化。采集柱塞泵不同运行时刻的振动信号,利用小波包对振动信号进行能量特征提取,结合信号时频域特征,构建柱塞泵健康状态特征数据集,由CNN-LSTM方法进行健康状态识别分类,并通过样本熵评估分类结果。为验证该健康评估方法的有效性,将其应用到柱塞泵的试验测试中,结果表明:该方法的识别准确率达到了99%,能够有效提高对柱塞泵健康状态评估的准确性。
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.
作者
魏娜莎
刘江锋
丁泽鹏
田志毅
WEI Na-sha;LIU Jiang-feng;DING Ze-peng;TIAN Zhi-yi(School of Vehicle and Traffic Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China)
出处
《科学技术与工程》
北大核心
2025年第21期8889-8897,共9页
Science Technology and Engineering
基金
山西省自然科学研究面上项目(202103021224087)
山西省科技重大专项计划“揭榜挂帅”项目(202401020101003)。
关键词
轴向柱塞泵
卷积神经网络
长短时记忆网络
健康评估
axial piston pump
convolutional neural network
long short-term memory network
health assessment
