Engineering

Engineering

ISSN Print: 1947-3931
ISSN Online: 1947-394X
www.scirp.net/journal/eng
E-mail: [email protected]
"Fault Prediction of Elevator Door System Based on PSO-BP Neural Network"
written by Penggao Wen, Meng Zhi, Guangyao Zhang, Shengmao Li,
published by Engineering, Vol.8 No.11, 2016
has been cited by the following article(s):
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[2] Adoption of AI in functional safety systems–smart elevator condition monitoring by the deep learning based data-driven modelling approach
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[3] Research on multi-objective-multi-elevator coordinative strategy based on neural network
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[6] Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı
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[7] Research on State Recognition Technology of Elevator Traction Machine Based on Modulation Feature Extraction
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[8] Smart Elevator Obstruction Detection System using Image Classification
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[9] Fault Prediction of Elevator Door Lock Based on MPGA-BP Algorithm
Journal of Physics: Conference Series, 2022
[10] Research on Non Intrusive Intelligent Monitoring System for Elevator State
… 17–20, 2021, Proceedings, Part II …, 2021
[11] Development of a hybrid artificial neural network-particle swarm optimization model for the modelling of traffic flow of vehicles at signalized road intersections
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[12] Research on Fault Prediction Based on Elevator Life Cycle Big Data
2021 International Conference on …, 2021
[13] 正负类双超球体模型在电梯故障诊断的应.
Information & Control, 2020
[14] Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency
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[15] Elevators Fault Diagnosis Based on Artificial Intelligence
2019
[16] The observer of a direct-current moto condition on the basis of the particles swarm method
… on Energy Web, 2019
[17] A Review of Particle Swarm Optimization
Journal of The Institution of Engineers (India): Series B, 2018
[18] 2361. Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model.
Journal of Vibroengineering, 2017
[19] Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model
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