Artikel ; Online: Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment.
Computational intelligence and neuroscience
2022 Band 2022, Seite(n) 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. |
---|---|
Mesh-Begriff(e) | Deep Learning ; Neural Networks, Computer ; Reproducibility of Results ; Research Design ; Technology |
Sprache | Englisch |
Erscheinungsdatum | 2022-06-29 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ZDB-ID | 2388208-6 |
ISSN | 1687-5273 ; 1687-5273 |
ISSN (online) | 1687-5273 |
ISSN | 1687-5273 |
DOI | 10.1155/2022/2455259 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
Zusatzmaterialien
Kategorien
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.