Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder
Model-based methods can be used to detect anomalies in industrial robots, but they require a high level of expertise and are therefore difficult to implement. The lack of sufficient data on the anomalous operation of industrial robots limits data-driven anomaly detection methods. This study proposes...
Saved in:
Main Authors: | ZhiDan Zhong, Yao Zhao, AoYu Yang, HaoBo Zhang, DongHao Qiao, ZhiHui Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2022/1179192 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection
by: Faleh Alshameri, et al.
Published: (2024-09-01) -
A convolutional autoencoder framework for ECG signal analysis
by: Ugo Lomoio, et al.
Published: (2025-01-01) -
Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention
by: Biao Yang, et al.
Published: (2018-01-01) -
A novel graph convolution and frequency domain filtering approach for hyperspectral anomaly detection
by: Yang Ding, et al.
Published: (2025-01-01) -
Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks
by: Selvakumar B, et al.
Published: (2025-02-01)