Fault Classification of Expressway Bridge Electromechanical System Using Fused Stack Sparse Long Short-Term Memory Networks

Abstract

The state of an expressway bridge’s electromechanical system is crucial to the safety and efficiency of the expressway. Efficient identification of faults in these systems facilitates timely operation and maintenance. Accurately and robustly classifying faults in the electromechanical systems of expressway bridges, given the vast data dimensions and limited fault samples, is a challenging task. In this paper, we comparatively study several typical neural network models. Firstly, we construct the electrical information matrix of the expressway bridge electromechanical system as a tree structure. Secondly, recurrent neural network, gated recurrent unit, and long short-term memory (LSTM) are exploited as base models for comparison. Thirdly, we propose a new network architecture called the fused stack sparse long short-term memory (FSS-LSTM) network, which incorporates sparsity constraints into multi-layer LSTM, and apply this model to the fault classification of expressway bridge electromechanical systems. Finally, comparative experiments using supervisory control and data acquisition (SCADA) data from the Taizhou Yangtze River Bridge in China are conducted. Experiment results demonstrate that the proposed FSS-LSTM network outperforms other models in fault classification, achieving macro-recall, macro-precision, and macro-F1 scores of 0.9344, 0.9283, and 0.9313, respectively. Among the three fault classes—strain, overcurrent, and other external failures—the strain fault is the most difficult to classify. The proposed FSS-LSTM network achieved over 92.61% accuracy for strain faults, and 97.88% and 97.12% accuracy for the other two classes, respectively.