On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment

With the rapid growth of elevator installations, conventional scheduled maintenance struggles to meet the dual demands of ensuring operational safety and cost control. This study proposes an innovative on-demand maintenance method that aligns with the Chinese policy directives on elevator maintenance reform. First, we conduct a historical fault cause analysis to identify the root causes of elevator entrapment incidents. Next, we establish an entrapment prediction model based on our historical data. Then, we design an elevator entrapment risk index report according to the prediction results. Finally, we formulate an on-demand maintenance plan that combines insights from the report with the conclusions of the cause analysis. Field implementation and comparative experiments demonstrate that the proposed on-demand maintenance method outperforms the scheduled one. The result shows significant reductions in accident and maintenance workload, justifying the practical value of this approach for the industry.