Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 3, Pages 2231-2241Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2999901
Keywords
Visualization; Tensors; Production; Videos; Quality assessment; Product design; Industrial intelligence; industrial Internet-of-Things (IIoT); recognition; tensor; visual feature
Categories
Funding
- National Key Research and Development Program of China [2018YFB1004001]
- NSFC (National Science Foundation of China) [61572057, 61836001]
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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.
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