Abstract
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 algorithm. This model addresses the challenges of feature redundancy and noise interference in elevator operation data, improving the stability and accuracy of fault predictions. Using a dataset of elevator operation parameters, including current, voltage, and running speed, the model utilizes the Attribute Correlation Density Ranking (ACDR) method for feature selection and the TSO-optimized VMD for denoising, enhancing data quality. Cross-validation and statistical analyses, including confidence interval calculations, were employed to validate the robustness of the model. The results demonstrate that the VMD-BILSTM-AEAM algorithm achieves a mean True Positive Rate (TPR) of 0.919 with a 95% confidence interval of 0.915 to 0.924, a mean False Positive Rate (FPR) of 0.090 with a 95% confidence interval of 0.087 to 0.092, and a mean Area Under the Curve (AUC) of 0.919 with a 95% confidence interval of 0.915 to 0.923. These performance metrics indicate a significant improvement over traditional and other deep learning models, confirming the model's superiority in predictive maintenance of elevators. The robust capability of the VMD-BILSTM-AEAM algorithm to accurately process and analyze time-series data, even in the presence of noise, highlights its potential for broader applications in predictive maintenance and fault detection across various domains.