Computer State Monitoring and Fault Intelligent Identification of Monitoring Electronic Equipment in Highway Environment

Tao Ma; Xinru Lu; Xie  Lie; Lie Jun

doi:10.6180/jase.202108_24(4).0014

Computer State Monitoring and Fault Intelligent Identification of Monitoring Electronic Equipment in Highway Environment

Computer Science and Information Engineering

Tao Ma¹ , Xinru Lu This email address is being protected from spambots. You need JavaScript enabled to view it.¹ , XieLei¹ , and LieJun¹

¹Shaanxi Province Communication Construction Anchuan Branch,Ankang 725000, Shanxi, China

Received: February 24, 2021
Accepted: March 23, 2021
Publication Date: August 1, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202108_24(4).0014

ABSTRACT

The monitoring system can improve the effective use of the expressway. It is a necessary part of ensuring the expressway to achieve high-speed, safety and comfort functions. It is also a necessary means to ensure the normal operation of the expressway. The construction of the monitoring system directly affects the operation management system and operation management mode. This article analyses the reasonable construction and function of the monitoring management system, expounds the reasonable selection and layout of vehicle detectors and cameras, and conducts in-depth research and analysis on the layout of variable information boards and variable speed limit signs. Aiming at the problem of the difference in the distribution of monitoring data caused by the fluctuation of the equipment working condition, which leads to the failure of the original diagnosis model and the reduction of the fault intelligent recognition rate, a deep domain adaptive method is proposed. Through deep feature mapping, interference factors of working conditions are eliminated, and insensitive features of working conditions are excavated. In addition, a method for determining the parameters of a deep mapping network is proposed to achieve the best domain adaptation effect. The test results show that, compared with artificial features, the representation features of deep network mining are more robust to data fluctuations; compared with shallow transfer learning, deep networks have stronger domain adaptability and cross-working fault diagnosis correct rate is also higher.

Keywords: Expressway; Monitoring electronic equipment; Deep confidence network; Fault intelligent identification; Simulated annealing algorithm

REFERENCES

[1] Iago Pachêco Gomes and Denis Fernando Wolf. Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis. Journal of Intelligent and Robotic Systems: Theory and Applications, 101(1), jan 2021. ISSN 15730409. .
[2] Md Shahjahan Hossain and Hossein Taheri. In Situ Process Monitoring for Additive Manufacturing Through Acoustic Techniques, oct 2020. ISSN 15441024.
[3] Debajyoti Misra, Gautam Das, and Debaprasad Das. An IoT based building health monitoring system supported by cloud. Journal of Reliable Intelligent Environments, 6(3): 141–152, 2020. ISSN 21994676.
[4] Dahui Li, Jianzhao Cui, and Qi Fan. Research on abnormal monitoring of vehicle traffic network data based on support vector machine. International Journal of Vehicle Information and Communication Systems, 5(2):247–264, 2020. ISSN 17418208.
[5] Bello Kontagora Nuhu, Ibrahim Aliyu, Mutiu Adesina Adegboye, Je Kyeong Ryu, Olayemi Mikail Olaniyi, and Chang Gyoon Lim. Distributed network-based structural health monitoring expert system. Building Research and Information, 49(1):144–159, 2021. ISSN 14664321.
[6] Han Zhang, Xuefeng Chen, Xiaoli Zhang, and Xinrong Zhang. A Bi-Level nested sparse optimization for adaptive mechanical fault feature detection. IEEE Access, 8: 19767–19782, 2020. ISSN 21693536.
[7] Dahui Li, Jianzhao Cui, and Qi Fan. Research on abnormal monitoring of vehicle traffic network data based on support vector machine. International Journal of Vehicle Information and Communication Systems, 5(2):247–264, 2020.
[8] Mohammed Abdulkarem, Khairulmizam Samsudin, Fakhrul Zaman Rokhani, and Mohd Fadlee A Rasid. Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction. Structural Health Monitoring, 19(3):693–735, may 2020. ISSN 17413168.
[9] Osamah Ibrahim Khalaf, F Ajesh, A. A. Hamad, Gia Nhu Nguyen, and Dac Nhuong Le. Efficient Dual-Cooperative Bait Detection Scheme for Collaborative Attackers on Mobile Ad-hoc Networks. IEEE Access, 2020. ISSN 21693536. .
[10] Muhammad Talha - Sana Azeem - Sohail - Javed -Rabia Tariq. Mediating effects of reflexivity of top management team between team processes and decision performance. Azerbaijan Journal of Educational Studies, 1(1):105–119, 2020.
[11] M. Talha, M. Sohail, and H. Hajji. Analysis of research on amazon AWS cloud computing seller data security. International Journal of Research in Engineering and Innovation, 4(3):131–136, 2020.