重庆大学学报2025，Vol.48Issue(6)：74-83,10.DOI:10.11835/j.issn.1000-582X.2025.06.007
CNN和双向编码解码LSTM融合的起重机械健康预测方法
Health prediction of lifting machinery based on CNN and bidirectional LSTM with encoder-decoder architecture
摘要
Abstract
To address challenges in multi-time-step health prediction for lifting machinery,such as short data spans,high-frequency measurements,multi-dimensional feature complexity,and limited labeled data,this paper proposes a hybrid method combining convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)networks with an encoder-decoder architecture(ED-BLSTM).The method begins by chronologically organizing monitoring data,followed by segmenting and reconstructing the dataset while maintaining consistent input-output time step sizes.The processed data is first fed into a CNN to extract the main features,generating a multi-dimensional feature matrix.This matrix then trains a BiLSTM network within an encoder-decoder framework to build a predictive model for multistep forecasting of machinery health status.Comparative experimental results show that the method reduces validation loss by 0.097%to 0.474%and prediction loss by 1.230%to 1.411%,outperforming current mainstream approaches.These results demonstrate its potential to advance predictive maintenance in industrial equipment.关键词
起重机械/健康预测/双向长短时循环神经网络/卷积神经网络/编码解码器Key words
lifting machinery/health prediction/bidirectional long short-term memory/convolutional neural network/encoder-decoder分类
计算机与自动化引用本文复制引用
陈宇豪,杨正益,文俊浩..CNN和双向编码解码LSTM融合的起重机械健康预测方法[J].重庆大学学报,2025,48(6):74-83,10.基金项目
国家重点研发计划(2024YFC3014900).Supported by the National Key R&D Program of China(2024YFC3014900). （2024YFC3014900）
