Design of Bank Server Fault Diagnosis System Based on Machine Vision

被引：0
|
作者：
Xu Jun [1 ]
Wu Shunyi [1 ]
Sun Mingxiao [1 ]
Luan Tiantian [1 ]
机构：
[1] Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA) | 2019年
关键词：
Fault detection; Feature extraction; Feature interpolation; Accuracy improvement; NEURAL-NETWORKS;
D O I：
10.1109/icma.2019.8816574
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes an automatic detection method for server operation based on image processing. The server's working status is determined by analyzing the image of the server status indicator. The image features are extracted by the Convolutional Neural Network (CNN), and the features of the detected area in the image are corrected by bilinear interpolation. The features of the detected area in the image are classified by the Multi-Layer Perceptron (MPL) to realize the server. It is verified through engineering that the recognition accuracy can reach 98.85%, and the recognition speed can reach about 40 frames per second, which can better meet the industrial needs.
引用
收藏
页码：1692 / 1697
页数：6
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