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计算机系统应用英文版:2024,33(11):247-256
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基于深度生存分析与SHAP的电梯风险预测
(1.华南师范大学 软件学院, 佛山 528225;2.广东省特种设备检测研究院 佛山检测院, 佛山 528041)
Elevator Risk Prediction Based on Deep Survival Analysis and SHAP
(1.School of Software, South China Normal University, Foshan 528225, China;2.Foshan Branch, Guangdong Institute of Special Equipment Inspection and Research, Foshan 528041, China)
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Received:April 21, 2024    Revised:May 20, 2024
中文摘要: 为解决低频、不规则时间周期的基于统计的电梯预测性维护问题, 本文提出了结合深度生存分析与数据切割、补偿的综合方案. 本文通过建立动态静态生存状态向量, 捕捉影响大型故障风险的因素; 此外, 针对记录型数据中存在的左删失问题, 本文采用数据补充的方式解决, 并探究不同补充方式与分割策略对深度生存模型精度的影响. 最后, 研究对电梯场景下的深度生存模型使用SHAP分析, 揭示各因素对故障风险的动态影响. 研究结果显示, 采用数据粗分割与Cox填充方式组合的模型具有强预测能力和准确性, DeepSurv模型在预测能力和稳定性上表现突出, 梯龄、提升高度对大型故障风险的贡献随特定条件的变化会发生转折.
Abstract: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.
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基金项目:广东省市场监督管理局科技项目 (2023CT06); 广东省基础与应用基础研究基金 (2020A1515110783); 2020年批次佛山高等教育高层次人才项目
引用文本:
曾倩欣,王槃,杨欢,杨勇.基于深度生存分析与SHAP的电梯风险预测.计算机系统应用,2024,33(11):247-256
ZENG Qian-Xin,WANG Pan,YANG Huan,YANG Yong.Elevator Risk Prediction Based on Deep Survival Analysis and SHAP.COMPUTER SYSTEMS APPLICATIONS,2024,33(11):247-256