Research on Elevator Fault Information Extraction and Prediction Diagnosis

Jiang, Xiaomei; Namokel, Michael; Hu, Chaobin; Tian, Ruifeng

doi:10.1007/978-981-33-6318-2_12

Cited by 4 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For accident prediction research, many scholars have utilized big data technologies [7], machine learning techniques [8], light gradient boosting machine [7], decision tree algorithms, etc., to predict elevator equipment failures [9]. Jin Lianghai et al [10] established a dynamic differential prediction model to forecast the accident occurrence rate of elevators.…”

Section: Introductionmentioning

confidence: 99%

Cause Analysis of Elevator Accidents Based on Complex Network

Wenhui Du, Yiwei Chen, Qian Zhou, Minghui Zhang, Ru Yi

2024

jes

View full text Add to dashboard Cite

In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.

show abstract