Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms

被引:43
作者
Lopez, Felipe [1 ]
Saez, Miguel [1 ]
Shao, Yuru [2 ]
Balta, Efe C. [1 ]
Moyne, James [1 ]
Mao, Z. Morley [2 ]
Barton, Kira [1 ]
Tilbury, Dawn [1 ]
机构
[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
Factory automation; intelligent and flexible manufacturing; FAULT-DETECTION; INTRUSION DETECTION; NETWORK INTRUSION; OUTLIER DETECTION; DIAGNOSTICS;
D O I
10.1109/LRA.2017.2714135
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
An important issue in anomaly detection in smart manufacturing systems is the lack of consistency in the formal definitions of anomalies, faults, and attacks. The term anomaly is used to cover a wide range of situations that are addressed by different types of solutions. In this letter, we categorize anomalies in machines, controllers, and networks along with their detection mechanisms, and unify them under a common framework to aid in the identification of potential solutions. The main contribution of the proposed categorization is that it allows the identification of gaps in anomaly detection in smart manufacturing systems.
引用
收藏
页码:1885 / 1892
页数:8
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