基于CNN-LSTM-CS工业管道腐蚀率预测模型

Corrosion Rate Prediction Model for Industrial Pipelines Based on CNN-LSTM-CS

针对传统工业管道腐蚀率预测模型存在特征提取依赖人工经验和泛化能力不足的问题,本文将卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory,LSTM)相结合,提出了基于布谷鸟优化算法(cuckoo search,CS)的CNN-LSTM-CS网络模型,实现对工业管道腐蚀率预测.首先,对采集的管道腐蚀数据集进行归一化预处理;然后,利用CNN网络提取影响管道腐蚀率因素的深层次特征信息,并通过训练LSTM网络构建CNN-LSTM预测模型;最后,采用CS算法对预测模型进行参数优化,减少预测误差,实现腐蚀率的精准预测.实验结果表明,对比几种典型的腐蚀率预测方法,本文提出的方法具有更高的预测精度,为工业管道腐蚀率检测提供新的思路.

The traditional prediction models for the corrosion rates of industrial pipelines often have the problems of dependence of feature extraction on artificial experience and insufficient generalization ability.To address this issue,this study combines the convolutional neural network(CNN) with the long short-term memory(LSTM) network and proposes a network model based on the cuckoo search(CS) optimization algorithm,namely,the CNN-LSTM-CS model,to predict the corrosion rates of industrial pipelines.Specifically,the collected pipeline corrosion dataset is pre-processed by normalization.Then,the CNN is used to extract information on the deep features of factors affecting the corrosion rates of the pipelines,and a CNN-LSTM prediction model is constructed by training the LSTM network.Finally,the CS algorithm is used to optimize the parameters of the prediction model,thereby reducing the prediction error and predicting the corrosion rate accurately.The experimental results show that compared with several typical prediction methods for the corrosion rate,the method proposed has higher prediction accuracy and provides a new approach for predicting the corrosion rates of industrial pipelines.