Anomaly Detection based on 1D-CNN-LSTM Auto-Encoder for Bearing Data

Authors: , ,

Abstract: The manufacturing industry is developing rapidly due to the Fourth Industrial Revolution. If a piece of bearing equipment, which is one of the essential parts of the manufacturing industry, fails, it will hinder the production of the manufacturing industry, which will lead to huge losses for the company. To prevent this, this paper implements a 1 Dimension-Convolution Neural Networks-Long Short-Term Memory (1D-CNN-LSTM) Auto-Encoder model for fault diagnosis of bearing data. The 1D-CNN-LSTM Auto-Encoder model showed high accuracy of 58 to 100 percent for eccentric bearing data that are difficult to visually diagnose as faults. In the future, we would like to extend this to a real-time failure diagnosis system that can remotely monitor the condition of the bearing equipment through real-time communication with the cloud server and test bed.

WSEAS Transactions on Information Science and Applications, ISSN / E-ISSN: 1790-0832 / 2224-3402, Volume 20, 2023, Art. #1

PDF
• HTML
DOI
XML
Cite
×
Citation Tools
WSEAS AMA
Daehee Lee, Hyunseung Choo, Jongpil Jeong, "Anomaly Detection based on 1D-CNN-LSTM Auto-Encoder for Bearing Data," WSEAS Transactions on Information Science and Applications, vol. 20, pp. 1-6, 2023, DOI:10.37394/23209.2023.20.1
Daehee Lee, Hyunseung Choo, Jongpil Jeong. Anomaly Detection based on 1D-CNN-LSTM Auto-Encoder for Bearing Data. WSEAS Transactions on Information Science and Applications. 2023;20:1-6. 10.37394/23209.2023.20.1
Copy to Clipboard
Citation copied to Clipboard

Certification

---