Intelligent instrument fault diagnosis and prediction system based on digital twin technology. Issue 1 (July 2021)

• Record Type: Journal Article
• Authors: Gao, Dawei; Liu, Peng; Jiang, Shengqian; Gao, Xiyu; Wang, Kun; Zhao, Anran; Xue, Yiming
• Is Part Of: Journal of physics. Volume 1983:Issue 1(2021)
• Publication Date: 2021-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1983/1/012106
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In the context of informatization and intelligent manufacturing systems, digital twin technology provides new technical ideas for intelligent decision-making. Aiming at instruments with high cost and low output, this paper develops low-cost high-efficiency fault diagnosis system to realize rapid feedback and fault of fault diagnosis results. The system includes three layers: data layer, control layer and output layer. In the data layer, this uses MEMS sensors and Zigbee wireless transmission network to construct a data link the physical end and the virtual model. In the control layer, this paper stores the collected twin data through cloud technology, extracts and calls relevant data according to the function module and then maps it to the output layer. In the output layer, this paper constructs a characteristics interpretation system, which divides the test set and training set through the dynamic database, to complete the classifier and results output. The results are fed back to the judgment framework and control layer to evaluate the effects of fault diagnosis and prediction, which provides a new method for model optimization.