基于K均值聚类算法和LSTM神经网络的管道腐蚀阶段预测方法
A Pipeline Stage Corrosion Prediction Method based on K-Means Clustering Algorithm and LSTM Neural Network
王新颖 1刘岚 1陈海群 1胡磊磊 1谢逢豪1
作者信息
摘要
针对声发射检测获得的管道腐蚀信号,提出了一种基于K均值(K-means)聚类算法和长短期记忆(LSTM)神经网络的管道腐蚀阶段预测方法.首先,利用K-means聚类算法将腐蚀信号分类,再构建LSTM神经网络模型,并采取了无监督学习的方式,以声发射波形为出发点,对模型进行参数优化,最后进行管道腐蚀阶段预测,并根据评价指标对模型进行评价.研究表明:对LSTM神经网络模型适当增加隐藏层,可以使得模型更加稳定,鲁棒性更好;与现有故障诊断模型相比,LSTM神经网络模型的精度更高.
Abstract
A pipeline corrosion stage prediction method based on K-means clustering algorithm and long short term memory(LSTM)neural network was proposed to use pipeline corrosion signals obtained by acoustic emission detection.First,the corrosion signals were classified by K-means algorithm,and then the LSTM neural network model was constructed.The model parameters were optimized by using acoustic emission waveform as the starting point through unsupervised learning.Finally,the pipeline corrosion stage was predicted and the model was evaluated according to evaluation indicators.The results show that LSTM neural network model could be more stable and more robust by adding hidden layers appropriately.Compared with existing fault diagnosis models,LSTM neural network model had higher accuracy.
关键词
声发射无损检测/腐蚀阶段预测/K-means聚类算法/长短期记忆(LSTM)神经网络/鲁棒性Key words
acoustic emission nondestructive testing/corrosion stage prediction/K-means clustering algorithm/long short term memory(LSTM)neural network/robustness引用本文复制引用
基金项目
中国石油-常州大学创新联合体(2021DQ06)
出版年
2024
NETL
NSTL
万方数据