Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers
The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM),...
Main Authors: | Tao Wang, Le Zhang, Xuefei Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
China Electrotechnical Society
2023-06-01
|
Series: | CES Transactions on Electrical Machines and Systems |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10032059 |
Similar Items
-
Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers
by: Chanthol Eang, et al.
Published: (2024-12-01) -
A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers
by: Zhenglong Sun, et al.
Published: (2023-10-01) -
Bearing fault diagnosis with parallel CNN and LSTM
by: Guanghua Fu, et al.
Published: (2024-01-01) -
Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
by: Seong Kyung Kwon, et al.
Published: (2021-11-01) -
Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods
by: Yaseen Ahmed Mohammed Alsumaidaee, et al.
Published: (2023-03-01)