Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/1/113 |
| _version_ | 1827384642659942400 |
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| author | Wei Pan Yi Xiang Weili Gong Haiying Shen |
| author_facet | Wei Pan Yi Xiang Weili Gong Haiying Shen |
| author_sort | Wei Pan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T15:02:00Z |
| format | Article |
| id | doaj.art-050f6109d13e402c8b601711f1ec9d8d |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T15:02:00Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-050f6109d13e402c8b601711f1ec9d8d2024-01-10T15:03:39ZengMDPI AGMathematics2227-73902023-12-0112111310.3390/math12010113Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning AlgorithmsWei Pan0Yi Xiang1Weili Gong2Haiying Shen3School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Mathematics and Statistics, Qinghai Normal University, Xining 810016, ChinaElevators 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.https://www.mdpi.com/2227-7390/12/1/113elevatorrisk evaluationfuzzy comprehensive evaluationmachine learning |
| spellingShingle | Wei Pan Yi Xiang Weili Gong Haiying Shen Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms Mathematics elevator risk evaluation fuzzy comprehensive evaluation machine learning |
| title | Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms |
| title_full | Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms |
| title_fullStr | Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms |
| title_full_unstemmed | Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms |
| title_short | Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms |
| title_sort | risk evaluation of elevators based on fuzzy theory and machine learning algorithms |
| topic | elevator risk evaluation fuzzy comprehensive evaluation machine learning |
| url | https://www.mdpi.com/2227-7390/12/1/113 |
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