Measuring in-service traction elevator reliability based on orthogonal defect classification and Markov analysis

Qiang Wang, Chongjun Yang, Juan Zhou, Jiaqi Xu, Benyao Chen, Kai Zhu, Linlin Wu, Xiaomeng Xu and Wanbing Su

Journal of Risk and Reliability, 2024, vol. 238, issue 6, 1271-1286

Abstract: To solve the challenge of accurate in-service traction elevator failure prediction, maintenance cycle and steady availability, a novel reliability model is proposed which combines orthogonal defect classification (ODC) and Markov analysis (MA). The elevator failure data are classified by the ODC method. Then, the failure rate and maintenance rate of the elevator parts are obtained based on triangular fuzzy theory. By analyzing the maintenance function of each part, the optimum maintenance cycle of the elevator is determined. Finally, the transient state and steady state equations are established by MA to determine the steady availability of elevators. A case study on elevator accidents and failure data is used to validate the effectiveness of the proposed method. The results show that the system steady state availability of elevators in the study was 0.9002.

Keywords: Traction elevator; orthogonal defect classification; triangular fuzzy theory; Markov analysis; reliability (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X231201193 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:238:y:2024:i:6:p:1271-1286

DOI: 10.1177/1748006X231201193

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().