# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 6 Conclusions  
This study proposes an on-demand maintenance method based on entrapment fault prediction to meet the elevator maintenance market's current demands and reduce passenger entrapment incidents. Based on historical fault cause analysis and field validation results, the following conclusions are drawn:  
(1) Statistical analysis of 19,930 historical fault records in City H confirmed that door system malfunction is the primary cause of elevator entrapment incidents, followed by human and external factors. This indicates that the door system is the key focus of on-demand maintenance strategies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \
\begin{tabular}{c} **Number of Maintenance Units** \\ **for Enterprise A** \\ \end{tabular} &
\begin{tabular}{c} **Number of Maintenance Units** \\ **for Enterprise B** \\ \end{tabular} & **Total** \\ \hline Scheduled maintenance & 228 units & 154 units & 382 units \\ On-demand maintenance & 34 units & 20 units & 54 units \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of the number of maintenance units in different maintenance methods. Figure 8: Experimental data of enterprise B: (**a**) test group data; (**b**) control group data. (2) We developed an entrapment prediction model based on Self-Attention and LSTM architectures with three years of elevator operation data from City H. Based on the model's predictive outputs, we designed an entrapment risk index report containing predicted entrapment probability values, risk level classification (red/green codes), and essential elevator information. (3) Implementing the on-demand maintenance plan derived from the risk index report and historical fault analysis, we conducted comparative field experiments including 2 maintenance enterprises, 14 project sites, and 762 elevators in City H. The experimental results demonstrate that implementing on-demand maintenance for high-risk \"red code\" elevators effectively reduces entrapment incidents. Additionally, the on-demand method significantly decreases the maintenance workload compared with conventional maintenance methods. Finally, we will conduct comparative experiments with existing methods proposed by other researchers in future work. (4) Although this study focuses on on-demand maintenance for elevator entrapment prevention, the proposed maintenance framework demonstrates transfer potential to other equipment systems, subject to adaptations in feature selection and model architecture tailored to specific device characteristics. Compliance with local regulations and adaptation to regional maintenance system characteristics are required for application in other fields. Conceptualization, T.C. and L.W. ([PERSON]); Methodology, T.C. ; Validation, Z.L. and Y.D. ; Investigation, T.C. ; Resources, L.W. ([PERSON]); Data Curation, T.C. ; Writing--Original Draft Preparation, T.C. ; Writing--Review and Editing, L.W. ([PERSON]); Supervision, L.W. ([PERSON]); Project Administration, Q.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Program Project of the State Administration for Market Regulation, grant number 2022 MK223, and the Science and Technology Program Project of Zhejiang Provincial Administration for Market Regulation, grant number ZC2023055. Not applicable. Restrictions apply to the availability of these data. Data were obtained from [Special Equipment Emergency Disposal Center, Hangzhou Special Equipment Inspection Science Research Institute] and are available [from the authors] with the permission of [Special Equipment Emergency Disposal Center, Hangzhou Special Equipment Inspection Science Research Institute]. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: