Self-testing Algorithm of Metro One-button Switch Station Equipment Based on a Neural Network

According to the operation and management requirements of subway stations, equipment managed by the station, such as lighting, escalators, broadcasting, etc., must be operated with one-button switch according to a certain process every day. The first step in one-button switch station operation is to...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 2747; no. 1; pp. 12036 - 12051
Main Authors: Chen, Yun, Xu, Xiaozhou, Zhang, Hao, Liao, Hongyun, Xia, Haiyang, Wu, Qian, Ren, Tianyang
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.05.2024
Subjects:
Algorithms
Artificial neural networks
Back propagation networks
Basic converters
Causal convolutional neural network
Efficiency
Escalators
Machine learning
Neural networks
One-button switch station
Self testing
Self-testing algorithm
Subway stations
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:According to the operation and management requirements of subway stations, equipment managed by the station, such as lighting, escalators, broadcasting, etc., must be operated with one-button switch according to a certain process every day. The first step in one-button switch station operation is to perform routine self-testing on these devices. Traditional self-testing operations mainly rely on manual handling by operators, which greatly affects their work efficiency. To optimize the operational processes, it is necessary to establish intelligent self-testing algorithms. An AC-CNN (Attention Causal Convolutional Neural Network) model to increase the self-testing efficiency in subway switching stations was established in this paper. AC-CNN model is a deep learning prediction network based on attention mechanism, which can capture the causal convolutional layer of the time before and after relationship as the basic network structure, optimize the convergence process through residual connections, and fuse global and local attention representations through matrix multiplication in the attention mechanism module to reduce useful information loss. The algorithm used the one-hot encoding and sliding window feature engineering operations to preprocess the historical self-testing operation data and completed the prediction of the self-testing information of metro one-button switch station equipment through the deep analysis of AC-CNN. The on-site application results indicated that AC-CNN had greater advantages in terms of accuracy and operation time compared to those of existing BP neural networks, GBDT, and LSTM algorithms, which can significantly improve the work efficiency of subway staff on one-button switch station.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2747/1/012036