# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 1 Introduction
[17] proposed a risk-based inspection method, a systematic decision-making technique to identify components prone to fault and assess their potential consequences. This approach was introduced into elevator maintenance because it balances cost-effectiveness and safety well. [PERSON] et al. [18] analyzed historical malfunction data and identified the top four causes: controller faults, car door issues, landing door defects, and faults of other car interior equipment. The study demonstrated that risk-based controller maintenance could extend elevator lifespan. [PERSON] et al. [19] established a defect risk factor index system for elevator drum brakes, analyzing hierarchical structures and intrinsic relationships among factors to identify critical maintenance points and reduce fault risks efficiently. [PERSON] et al. [20] developed an elevator condition diagnosis model based on a simple vibration zone segmentation method. [PERSON] et al. [21] validated the proposed fusion model during elevator installation and maintenance management. The model utilized a dataset of 110,698 safety inspection records from 25,729 construction sites (including elevator installation and maintenance sites) managed by an elevator enterprise to construct a hazard correlation network. This network enabled the identification of critical on-site hazards for proactive construction management. [PERSON] et al. [22] introduced an intelligent defect diagnosis method for elevator traction induction motors based on a decision fusion system. [PERSON] et al. [23] proposed a practical solution for defect diagnosis in smart building elevators, enabling maintenance personnel to detect malfunctions based on objective evidence rather than intuition. [PERSON] et al. [24] developed a novel damage identification method for elevator systems,which combines vibration data obtained from physical measurements and high-fidelity multibody dynamics models with deep learning algorithms. [PERSON] et al. [25] presented a GNN-LSTM-BDANN deep learning model for variations in operating environments to ensure sound signal acquisition. The model leveraged historical acoustic data from other elevators to predict the remaining lifespan of target elevator door systems. To prevent mechanic accidents, [PERSON] et al. [9] explored an IoT-based elevator fault monitoring system using the Relief-F algorithm to assess potential influencing factors. The studies above compared different methods and improved predictive methods based on fault prediction research to obtain optimal results. However, applying predictive outcomes to subsequent maintenance operations remains unstudied. Some references focus only on specific elevator components or certain critical parts, which cannot effectively support the implementation of on-demand maintenance. To mitigate the above issues, this paper proposes an elevator on-demand maintenance method based on the prediction of entrapment faults. First, using City H's 2023 elevator fault records, the study identified the main causes of entrapment problems. Second, a hybrid model for elevator fault prediction was developed based on data from City H's Elevator Emergency Response Center, enabling the forecasting of elevator entrapment events. Finally, an on-demand maintenance plan was implemented by integrating the predicted results with fault analysis findings. Comparative experiments with scheduled maintenance methods demonstrated that this approach effectively reduces the occurrence of entrapment incidents while decreasing maintenance workload. The contributions of this paper are summarized as follows:  
(1) Analysis of the 2023 historical fault data revealed that door system faults, human-related faults, and external factors are the three main causes of entrapment incidents. (2) By applying the prediction model for entrapment events, a risk index checklist was designed, including predicted entrapment values, risk levels (red/green codes), and basic elevator information, which achieves multidimensional data visualization.