AUTHOR=Jiang Leyun , Li Ye , Yang Dongxing , Hu Jian , Xu Jun , Xie Jianghong , Cai Yi 

TITLE=Cable laying conveyor state prediction and fault warning based on attention-driven CNN-LSTM algorithm

JOURNAL=Frontiers in Mechanical Engineering

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1549053

DOI=10.3389/fmech.2025.1549053

ISSN=2297-3079

ABSTRACT=The monitoring condition of the cable laying conveyor, such as rotational speed, driving current, and side pressure, reflects the real-time operation status of the cable laying process. Accurate prediction of multiple monitoring states of cable conveyors can assess the status of cable laying in advance and avoid failure. The existing cable laying construction mainly relies on the threshold value to determine the safety status. It rarely predicts the state and does not consider the connection between the various monitoring states, so it is difficult to make accurate predictions. For this reason, this paper proposes an attention-driven Convolutional Neural Network-Long Short Term Memory (A-CNN-LSTM) algorithm for multi-state prediction and fault warning of cable conveyor, which explores the relationship between the states of the cable conveyor and makes a more accurate prediction. CNN is used to mine the connection between the states of the cable conveyor and the attention mechanism is used to intelligently allocate weights. LSTM is used to explore the law of the states of the conveyor over time, and use the attention mechanism to intelligently allocate weights in the time step, which ultimately realizes the prediction. The method is applied to a 110 kV cable laying experiment and compared with the prediction results of the widely used TCN algorithm, and the CNN-RNN algorithm without attention mechanism, which shows that the proposed attention-driven prediction algorithm has higher accuracy, better reflects the connection between multiple monitoring states of the cable conveyor, and performs more accurate prediction.