MCU System-based Intelligent High-speed Elevator Door Operator Fault Analysis and Research

Wang, Qibing; Leng, Yonggang; Li, Dongliu; Zhang, Xiaoqiang; Ren, Li; Hongmei, Zhu; Zhang, Hao

doi:10.1088/1757-899x/428/1/012028

Cited by 3 publications

(1 citation statement)

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“…For instance, an abnormality in the opening mechanism can result in passengers being trapped, while failure to close before elevator movement may precipitate shearing or pinching incidents. According to statistical data, more than 80% of all elevator accidents are attributed to door system malfunctions [1]. To determine the state of elevator door systems, KIM et al [2] proposed a knowledge-based feature operation.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning

Pan,

Shao,

Dai

et al. 2024

Sensors

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The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

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Section: Introductionmentioning

confidence: 99%