基于自动编码器的离心泵空化状态识别模型研究
Research on a Centrifugal Pump Cavitation State Recognition Model Based on Autoencoder
张彤赫 1葛秉鑫 1马桤政 1刘正杨 1宋永兴1
作者信息
- 1. 山东建筑大学热能工程学院,济南 250100
- 折叠
摘要
空化状态识别是离心泵状态监测的难点之一.为了提高空化状态识别的效果,提出了一种基于自动编码器的离心泵空化状态识别方法.对离心泵的空化进行了实验研究,在三个温度下分别采集了六个工频上泵壳的振动信号.对原始信号带做时频域分析,得到时频图;通过无监督训练自动学习输入数据的特征;并利用自动编码器对离心泵的四类空化状态进行识别.研究表明,基于该模型能有效地提取离心泵空化振动信号的特征并识别,识别成功率达到94.58％,比传统的快速傅里叶-支持向量机模型提高了 14.58％.
Abstract
The identification of cavitation state is one of the challenging aspects of centrifugal pump condition monitoring.In order to improve the effectiveness of cavitation state identification,a method based on autoencoder for centrifugal pump cavitation state identification is proposed.Experimental studies on cavitation of the centrifugal pump were conducted,and vibration signals of the pump casing at work frequencies were collected at three different temperatures.The original signals were subject to time-frequency domain analysis to obtain time-frequency graphs.The unsu-pervised training was used to learn the features of the input data,and an autoencoder was employed to identify the four types of cavitation states in the centrifugal pump.The research shows that this model can effectively extract the features of the centrifugal pump cavitation vibration signals and achieve a successful identification rate of 94.58％,which is 14.58％higher than the traditional Fast Fourier Transform-Support Vector Machine model.
关键词
离心泵/空化状态识别/自动编码器/振动信号Key words
centrifugal pump/identification of cavitation status/autoencoder/vibration sign引用本文复制引用
基金项目
山东省自然科学基金(ZR2021QE157)
国家压缩机技术重点实验室开放基金(SKL-YSJ202108)
出版年
2024
NETL
NSTL
万方数据