压力容器设备可靠性评估与剩余寿命预测

Reliability Assessment and Remaining Life Prediction of Pressure Vessel Equipment

针对传统腐蚀管道预测效率低及精度低的问题,提出一种随机森林算法(RF)、思维进化法(MEA)及Elman相结合的模型(即RF-MEA-Elman模型):首先采用RF对管道数据预处理,运用MEA对Elman神经网络的权值和阈值参数进行寻优,以此建立腐蚀管道剩余寿命组合预测模型。选取某一管段为例,借助MATLAB进行仿真训练与预测,结果表明,该模型与其他两种传统单一模型相比误差小且有更高的预测精度及泛化能力,为管道剩余寿命研究提供了新思路,也为LNG接收站风险防范和维修管理提供了参考依据。

Aiming at the problems of low efficiency and accuracy in traditional corrosion pipeline prediction,a model combining Random Forest Algorithm(RF),Mind Evolution Algorithm(MEA),and Elman(i.e.RF-MEA Elman model)is proposed.Firstly,RF is used to preprocess pipeline data,and MEA is used to optimize the weight and threshold parameters of Elman neural network,in order to establish a combined prediction model for the remaining life of corrosion pipelines.Taking a certain pipeline section as an example,the simulation training and prediction are carried out with MATLAB.The results show that this model has smaller error and higher prediction accuracy and generalization ability compared with the other two traditional single models,which provides a new idea for the study of the remaining life of the pipeline,and also provides a reference for the risk prevention and maintenance management of the LNG terminal.