# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 1 Introduction  
According to the \"Administration for Market Regulation Announcement on National Special Equipment Safety Status 2024,\" the Chinese elevator inventory reached 11.5324 million units by the end of 2024. Elevators play a critical role in the eight major categories of special equipment [1]. However, the rapid expansion of the elevator industry has also led to serious safety challenges [2]. The maintenance methods and schedules of elevators directly impact the safety, stability, and reliability of operation [3, 4, 5]. The scheduled maintenance approach is inefficient, inconsistent, underqualified, and delayed and therefore significantly impairs elevator safety [6, 7]. In 2024, there were 132 incidents involving special equipment nationwide, including 41 elevator accidents that accounted for 31.06% of all notable equipment accidents, resulting in 27 deaths [1]. Although the accident rate of 0.001% may seem statistically insignificant, the number of fatalities remains alarmingly high. To mitigate the safety issues and stabilize elevator operation while reducing the accident rate, the Chinese government has initiated maintenance regime reforms, promoting the transition from scheduled to on-demand maintenance. Currently, implementing on-demand maintenance in China remains in the pilot exploration phase. Multiple regions have adopted methods such as \"Self-reporting with credit-based supervision\" systems and\"IoT-enabled predictive maintenance\" platforms, integrated with elevator liability insurance schemes, to facilitate with the implementation of on-demand maintenance. Although these measures have enhanced maintenance efficiency, certain operational limitations still persist [8; 9]. With the iterative advancement of industrial technology, equipment maintenance methods have developed through three distinct phases: breakdown-based reactive maintenance, scheduled preventive maintenance, and predictive maintenance (PdM) [10]. The on-demand maintenance currently utilized for elevators in China essentially represents an application of predictive maintenance theory within the elevator domain [11; 12]. PdM has emerged as the predominant operational maintenance methodology, providing significant advantages, including enhanced equipment reliability, improved operational status and performance, and reduced maintenance costs. This approach has been widely implemented across multiple industrial fields, including mechanical engineering, aerospace, nuclear power, maritime, and wind energy. The key to predictive maintenance lies in accurately forecasting the future state of equipment and promptly implementing maintenance measures under different conditions [13]. Maintenance technology demonstrates a clear trend of formulating decisions based on multivariable condition monitoring and fault prediction [14]. Against the backdrop of rapid advancements in information technology, machine learning and deep learning algorithms have been widely applied and continuously developed in the fields of fault diagnosis and fault prediction. For example, [PERSON] et al. [15] integrated probabilistic Remaining Useful Life (RUL) prediction into maintenance scheduling using Convolutional Neural Networks (CNNs), providing optimal timing for component replacement. [PERSON] et al. [16] developed a novel dynamic PdM framework utilizing Long Short-Term Memory (LSTM) networks to predict defects. The authors thoroughly discussed the advantages of PdM over other maintenance strategies and presented a comprehensive framework spanning data-driven prediction to maintenance decision-making. [PERSON] et al. [2] employed the Decision-Analytic Network Process to clarify the connections among various indicators, calculate their weights within the system, and derive a comprehensive evaluation score rating elevator health. The assessment model was applied to practical experiments to evaluate elevators and verify their effectiveness. [PERSON] et al.