research-article

Failure Prediction of Elevator Running System Based on Knowledge Graph

Authors:

Xue-Qin JiangAuthors Info & Claims

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

Pages 53 - 58

https://doi.org/10.1145/3414274.3414283

Published: 26 August 2020 Publication History

Abstract

As the number of buildings in modern cities continues to increase, the usage of the elevator is becoming pervasive. Accurate prediction and early warning of the failure of the elevator running system can reduce effectively the consumption of elevator maintenance resources and manpower. However, due to the repetition of failure data, the weak relationship and slow update of the data, the system of failure prediction and early warning of elevator need new patterns to avoid information data bias and improve the efficiency of data storage and extraction. We analyze the existing failure prediction methods of the elevator running system and understand that knowledge graph has the characteristic of describing concepts and their interrelationships in the physical world in symbolic form. Furthermore, by associating the failure phenomena and causes and other relevant factors, we construct the knowledge graph of failure of the elevator running system and explain the process and steps of the failure prediction.

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Cited By

Shang YRen YPeng HWang YWang GLi ZYang YLi Y(2023)A Perspective Survey on Industrial Knowledge Graphs: Recent Advances, Open Challenges, and Future Directions2023 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC58545.2023.10327989(194-200)Online publication date: 9-Jul-2023
https://doi.org/10.1109/ICMLC58545.2023.10327989
Xia LZheng PLi XGao RWang L(2022)Toward cognitive predictive maintenance: A survey of graph-based approachesJournal of Manufacturing Systems10.1016/j.jmsy.2022.06.00264(107-120)Online publication date: Jul-2022
https://doi.org/10.1016/j.jmsy.2022.06.002

Index Terms

Failure Prediction of Elevator Running System Based on Knowledge Graph
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks

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cover image ACM Other conferences

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

July 2020

261 pages

ISBN:9781450376044

DOI:10.1145/3414274

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Natl University of Singapore: National University of Singapore
SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2020

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National Key Research and Development Program of China

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DSIT 2020

DSIT 2020: 2020 3rd International Conference on Data Science and Information Technology

July 24 - 26, 2020

Xiamen, China

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DSIT 2020 Paper Acceptance Rate 40 of 97 submissions, 41%;

Overall Acceptance Rate 114 of 277 submissions, 41%

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Cited By

Shang YRen YPeng HWang YWang GLi ZYang YLi Y(2023)A Perspective Survey on Industrial Knowledge Graphs: Recent Advances, Open Challenges, and Future Directions2023 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC58545.2023.10327989(194-200)Online publication date: 9-Jul-2023
https://doi.org/10.1109/ICMLC58545.2023.10327989
Xia LZheng PLi XGao RWang L(2022)Toward cognitive predictive maintenance: A survey of graph-based approachesJournal of Manufacturing Systems10.1016/j.jmsy.2022.06.00264(107-120)Online publication date: Jul-2022
https://doi.org/10.1016/j.jmsy.2022.06.002

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