This study proposes a comprehensive solution that combines deep survival analysis, data segmentation, and data imputation to address the issue of statistical predictive maintenance for elevators, which is characterized by low frequency and irregular time periods. This study establishes both dynamic and static survival vectors to capture factors influencing major fault risks. Additionally, to tackle left censoring in recorded data, this research employs data imputation and explores the impact of different imputation methods and segmentation strategies on the accuracy of deep survival models. Finally, this study utilizes SHAP to analyze deep survival models in elevators to reveal the dynamic influence of various factors on fault risks. The results indicate that a model combining rough data segmentation with Cox imputation demonstrates strong predictive capability and accuracy. The DeepSurv model excels in predictive capability and stability. The contribution of factors such as elevator age and lifting height to major fault risks can shift under specific conditions.