Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations

Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault pr...

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Bibliographic Details
Main Authors: Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan, Gang Yu
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Applied Sciences
Subjects:
platform screen door system
fault prediction
sparse and weak feature data
data augmentation
CNN-LSTM
Online Access:https://www.mdpi.com/2076-3417/14/18/8156
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