Reliability Evolution of Elevators Based on the Rough Set and the Improved TOPSIS Method

Zhang, Jian; Zhao, Xiangang; Liu, Canmian; Wang, Wei

doi:10.1155/2018/4784965

Cited by 6 publications

(2 citation statements)

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“…Zhang et al (2018) developed an evaluation index system to assess equipment reliability from multiple perspectives, including people, management, equipment, and the environment. They constructed an improved TOPSIS evaluation model that combined gray number theory with the TOPSIS method [6]. By combining material element theory and other methods, Sun et al (2020) proposed a scalable fuzzy integrated evaluation model for elevator safety [7].…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al Scholars have introduced the application of fuzzy mathematics in elevator evaluation due to the complexity of elevator systems. Zhang et al (2018) developed an evaluation index system to assess equipment reliability from multiple perspectives, including people, management, equipment, and the environment. They constructed an improved TOPSIS evaluation model that combined gray number theory with the TOPSIS method [6].…”

Section: Introductionmentioning

confidence: 99%

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Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms

Pan,

Xiang,

Gong

et al. 2023

Mathematics

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Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model’s performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy.

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