Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
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Elevator Fault Classification Using Deep Learning Model

딥러닝 모델을 활용한 승강기 결함 분류

  • Young-Jin, Jung (Department of Industrial Engineering, Inha University) ;
  • Chan-Young, Jang (Department of Industrial Engineering, Inha University) ;
  • Sung-Woo, Kang (Department of Industrial Engineering, Inha University)
  • 정영진 (인하대학교 산업경영공학과) ;
  • 장찬영 (인하대학교 산업경영공학과) ;
  • 강성우 (인하대학교 산업경영공학과)
  • Received : 2022.10.01
  • Accepted : 2022.12.21
  • Published : 2022.12.31
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Abstract

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.

Keywords

Acknowledgement

이 연구는 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2022H1D8A3037396) 이 연구는 2022년도 산업통산자원부 및 산업기술평가원(KEIT) 연구비 지원에 의한 연구임(20011249)

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