基于人工神经网络的离心泵空化故障诊断
Centrifugal Pump Cavitation Fault Diagnosis Based on Artificial Neural Network
刘朝玺 1叶志锋 1王彬 1严社斌2
作者信息
- 1. 南京航空航天大学,江苏 南京 210016
- 2. 贵州红林航空动力控制科技有限公司,贵州 贵阳 550009
- 折叠
摘要
为了研究离心泵空化故障诊断问题,探究人工神经网络在该问题上的预测效果,通过数值仿真的方法对离心泵流场进行模拟,采集不同状态下流场内各点压力值及工作点作为输入特征,以旋转区域气体体积分数为标签特征,对离心泵空化状态进行神经网络建模.使用LSTM和一维卷积网络处理时序数据,并在特征提取阶段添加正则化损失函数以保证网络稀疏性.最终模型在测试集上的分类任务准确率达到 95%以上,能够有效地对离心泵空化程度进行诊断.
Abstract
In order to study the cavitation fault diagnosis of centrifugal pump and explore the prediction effect of artificial neural network concerning the fault diagnosis,the flow field of centrifugal pump was simulated by numerical simulation,the pressure values and working points of various points in the downstream field in different states were collected as the input characteristics and the volume fraction of the gas in the rotating region was taken as the label characteristic to conduct the neural network modeling for the cavitation state of the centrifugal pump.The LSTM and one-dimensional convolutional network were used to process the time series data,and the regularization loss function was added in feature extraction stage to ensure network sparsity.The accuracy rate of classification task of the trained model on the test set exceeded 95%,which can effectively diagnose the cavitation degree of centrifugal pump.
关键词
离心泵/空化/人工神经网络/自编码器Key words
centrifugal pump/cavitation/artificial neural network/autoencoder引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据