摘要
Abstract
Due to the high precision and complex manufacturing processes of oilfield equipment,the supervision during manufacturing is particularly crucial.To enhance the reliability and efficiency of manufacturing supervision,this study proposes a fault prediction model named ACL-OFE-FP(Attention-based CNN-LSTM Oil Field Equipment Fault Prediction)that integrates CNN,LSTM,and Multi-Head Self-Attention(MSA)mechanisms.The ACL-OFE-FP model combines CNN with multi-level interconnected LSTM networks and introduces a feature fusion mechanism based on oilfield equipment data distribution and temporal characteristics.It further enhances feature representation through a multi-head self-attention module,making it suitable for fault prediction in oilfield equipment manufacturing supervision.Trained and tested on 21 000 sets of continuous production data,the results demonstrate that the ACL-OFE-FP model reduces the Mean Squared Error(MSE)by over 30%compared to traditional CNN networks in capturing temporal features and improving fault prediction accuracy.This study provides an effective solution for intelligent fault prediction in oilfield equipment manufacturing supervision and advances the digital and intelligent transformation of oilfield equipment supervision.关键词
油田装备/故障预测/卷积神经网络/长短期记忆网络/多头自注意力机制Key words
oilfield equipment/fault prediction/convolutional neural network/long short-term memory/multi-head self-attention分类
计算机与自动化
