Article ; Online: Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment.
Computational intelligence and neuroscience
2022 Volume 2022, Page(s) 2455259
Abstract: Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world ... ...
| Abstract | Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model's online detection and update. The method's efficacy is demonstrated by experiments based on real data from power plant sensors. |
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| MeSH term(s) | Deep Learning ; Neural Networks, Computer ; Reproducibility of Results ; Research Design ; Technology |
| Language | English |
| Publishing date | 2022-06-29 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2388208-6 |
| ISSN | 1687-5273 ; 1687-5273 |
| ISSN (online) | 1687-5273 |
| ISSN | 1687-5273 |
| DOI | 10.1155/2022/2455259 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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