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.

References

[1]

LIN Sui-xian. Elevator Pre Diagnosis in the Time of Big Data[J]. Mechanical & Electrical Engineering Technology, 2016, 45(07):159--160+188.

[2]

ZHANG Zhenzhen, TAN Hao. New model of elevator supervision based on big data[J]. Electronic Technology & Software Engineering, 2016(22):164--165.

[3]

Y. Tang, T. Liu, G. Liu, J. Li, R. Dai and C. Yuan, "Enhancement of Power Equipment Management Using Knowledge Graph," 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), Chengdu, China, 2019, pp. 905--910.

[4]

XU Bin, LI Lin, Zhong Luo. Design of Intelligent Elevator Analysis Prealarming Platform based on Big-data[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2017, 41(02):359--362.

[5]

LU Erwei. APPLICATIONS OF DATA MINING TECHINIQUES IN ELEVATOR EMERGENCY DISPOSAL AND SAFETY SUPERVISION [D]. Southeast University, 2017.

[6]

DUAN Deng, QIU Yi-Min, ZHOU Li. Fault Predication of Multi-Elevator Operating System Based on Neural Network[J]. Computer Systems & Applications, 2011, 20(09):252--255.

[7]

ZHANG Yi, ZHANG Dongyue, ZHAO Yibo. Design and Implementation of Remote Elevator Fault Warning System Based on Large Data[J]. Modern Information Technology, 2019, 3 (06):169--170+173.

[8]

Feng Yongming. The Research of Elevator Safety Big Data Mining Based on Hadoop[D]. Xi'an University of Science and Technology, 2017.

[9]

CHANG Shan. The Mining Algorithm Research of Elevator Safety Big Data Based on Hadoop[J]. Jourmal of Weinan Normal University, 2018, 33(08):34-40+66.

[10]

ZHONG Qilong. Research on Application of Elevator Early Warning in Big Data Era[J]. INFORMATION & COMMUNICATIONS, 2018(12):276--278.

[11]

Fu Xiaorong. Research and Implementation of Elevator Safety Monitoring, Data Analysis and Mining[D]. Yangtze University, 2018.

[12]

MengQi Zeng. DESIGN AND IMPLEMENTATION OF MAIN FACTOR ANALYSIS OF ELEVATOR FAILURE AND EARLY WARNING SUBSYSTEM OF NATIONAL SPECIAL EQUIPMENT SAFETY SUPERVISION SYSTEM [D]. BEJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS, 2018.

[13]

XU Zeng-lin, SHENG Yong-pan, HE Li-rong, and WANG Ya-fang. Review on Knowledge Graph Techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, Volume 45(4):589--606.

[14]

R.Xie, Z. Liu, J. Jia, H. Luan, and M. Sun, "Representation learning of knowledge graphs with entity descriptions," in 16th AAAI Conference on Artificial Intelligence (AAAI-16), Feb. 2017, pp. 2659--2665.

[15]

P.Nistala and P. Kumari, "Establishing content traceability for software applications: An approach based on structuring and tracking of configuration elements," in 7th Intl. Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), May 2013, pp. 68--71.

[16]

WEI Jin, LI Wei-hua, PAN Wei. Research on Intelligent Decision Support Technology and Application Based on Knowledge Graph[J/OL]. COMPUTER TECHNOLOGY AND DEVELOPMENT, 2020(01):1--6 [2019-10-07]. http://kns.cnki.net/kcms/detail/61.1450.TP.20190925.1521.028.html.

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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