Experimental dataset.

<div><p>This study proposes an advanced elevator fault precursor prediction method integrating Variational Mode Decomposition (VMD), Bidirectional Long Short-Term Memory (BILSTM), and an Autoencoder with an Attention Mechanism (AEAM), collectively referred to as the VMD-BILSTM-AEAM algor...

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Bibliographic Details
Main Author:	Hao Cao (2248066) (author)
Other Authors:	Xiaoyan Du (171801) (author)
Published:	2025
Subjects:	Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified performance metrics indicate elevator operation parameters deep learning models enhancing data quality elevator operation data model &# 8217 aeam algorithm achieves aeam algorithm series data ae algorithm xlink "> term memory study proposes statistical analyses significant improvement running speed robust capability results demonstrate predictive maintenance model utilizes model addresses mean area including current improved lstm feature selection feature redundancy fault predictions confidence interval broader applications attention mechanism analyze time accurately process
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Experimental dataset.

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