SDBOSR: Separable Decision Boundary based Open Set Recognition for Manufacturing Equipment Fault Classification

被引：1
作者：
Yoon, Jeongseop [1 ]
Kim, Donghwan [1 ]
Kim, Daeyoung [1 ]
机构：
[1] BISTelligence, Res Team, Seoul, South Korea
来源：
2022 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM) | 2022年
关键词：
fault classification; open set recognition; deep learning; DIAGNOSIS;
D O I：
10.1109/ICPHM53196.2022.9815748
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Equipment fault may cause serious losses to environmental, economic, and safety. To reduce losses, the requirements for industrial equipment diagnostic systems are rapidly increasing. However, because, industrial equipment is usually complex and large in number, it is difficult to define all types of fault in advance. To reduces the loss, it is necessary to accurately classify the higher the severity level of the fault. In this paper, we propose an algorithm that can classify a fault type of equipment and classify the unknown type based on the open ser recognition method. The proposed method was applied to the AHU dataset and showed improved results compared to another algorithm.
引用
收藏
页码：121 / 126
页数：6
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