工矿自动化2026，Vol.52Issue(8)：71-77,7.DOI:10.13272/j.issn.1671-251x.2026020019
基于CNN-BiLSTM-Attention的刮板输送机断链故障诊断
Chain breakage fault diagnosis in scraper conveyors based on CNN-BiLSTM-Attention
摘要
Abstract
Existing deep learning approaches to chain fault diagnosis in scraper conveyors mostly use a single neural network model.They have limitations including a narrow range of extracted features,insufficient extraction of deep latent fault features,and inadequate fault representation and generalization,making it difficult to fully capture complex fault characteristics during chain operation.To address these problems,a hybrid neural network model integrating a Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(BiLSTM),and an Attention mechanism(Attention)for chain breakage fault diagnosis in scraper conveyors was proposed and applied to chain breakage fault diagnosis in scraper conveyors.First,operating data,including the rotational speed,torque,and current of the head and tail motors,and chain tension data were collected.The raw data were then preprocessed by outlier removal,linear interpolation,low-pass filtering,and downsampling to obtain high-quality continuous time series.Finally,the preprocessed data were input into the CNN-BiLSTM-Attention model.The CNN extracted local fault features from the signals,BiLSTM captured long-range temporal dependencies,and Attention adaptively increased the weights of key features,enabling accurate identification of three states:normal operation,incipient chain breakage,and chain breakage.Experimental results showed that the CNN-BiLSTM-Attention model outperformed the single neural network models in accuracy,recall,and F1 score,achieving 96.92％,96.77％,and 96.75％,respectively.It also achieved the highest classification accuracy for each of the three chain states.These results indicate that the model can effectively reduce the false-alarm rate in scraper chain breakage fault diagnosis.关键词
刮板输送机/断链故障诊断/CNN/BiLSTM/注意力机制Key words
scraper conveyor/chain breakage fault diagnosis/CNN/BiLSTM/Attention mechanism分类
矿业与冶金引用本文复制引用
解鸿章,范宝冬,陈伟,刘二毛,李致远,赵欢..基于CNN-BiLSTM-Attention的刮板输送机断链故障诊断[J].工矿自动化,2026,52(8):71-77,7.基金项目
国家自然科学基金资助项目(U23B2092) （U23B2092）
国家重点研发计划课题资助项目(2017YFF0210606). （2017YFF0210606）
