期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 3, 页码 2231-2241出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2999901
关键词
Visualization; Tensors; Production; Videos; Quality assessment; Product design; Industrial intelligence; industrial Internet-of-Things (IIoT); recognition; tensor; visual feature
类别
资金
- National Key Research and Development Program of China [2018YFB1004001]
- NSFC (National Science Foundation of China) [61572057, 61836001]
The Industrial Internet-of-Things has transformed industrial manufacturing by incorporating production equipment, mobile terminals, and smart devices with networks, and the processing and recognition methods of industrial visual information are crucial for providing industrial intelligence.
Industrial Internet-of-Things (IIoT) has revolutionized almost every aspect of industrial manufacturing through industrial intelligence by incorporating production equipment, mobile terminals, and smart devices with wireless or wired networks. However, industrial visual information, such as images, videos, graphs, and texts, generated and collected from the industrial processes, contains various kinds of hidden value for industrial intelligence. Therefore, for the trend of providing ubiquitous industrial intelligence, new paradigms of perception and processing technologies of visual information such as recognition methods are required. However, industrial visual information is heterogeneous and complex with multiattributes, which presents significant challenges on visual information perception and processing technologies such as multiattributes recognition method. In this article, to provide industrial intelligence, a tensor-based visual feature recognition method is used to recognize the object from the perspective of multiattributes with the combination of attributes. To demonstrate its practical implementation, a case study about the industrial intelligence on the faulty location and diameter of bearings in the IIoT is described. Also, experiments on object recognition are carried out on the public image set COIL-100 to demonstrate the performance of the proposed method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据