• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.8
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• pp.1278-1284
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• 2017

As of December 2016, there are 608,828 elevators in operation in Korea and 179,790 elevators in more than 15 years. 30.4% of all elevator are aging. Improved maintenance of the elevator and proactive replacement of the parts of the elevator can extend the lifetime of the elevator and ensure safety. An unclean environment reduces the lifetime of elevator parts. If you do not clean the environment and prevent preventive parts replacement, eventually shortening the lifetime of the parts connected to the failed part or causing more damage will result in greater economic loss. Also, the risk of elevator safety accidents due to failures of elevator parts will be increased accordingly. The study of optimum replacement time of elevator parts will contribute to prevention of safety accident of elevator and prolongation of lifetime of elevator through preventive replacement of elevator parts.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.1-8
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• 2022

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users' safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.