Paper Info
Title
Aesgru: An Attention-Based Temporal Correlation Approach For End-To-End Machine Health Perception
Abstract
Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2943381
IEEE ACCESS
Keywords
DocType
Volume
Logic gates, Correlation, Computational modeling, Feature extraction, Predictive models, Training, Computer architecture, Health perception, temporal correlation, gated recurrent unit networks, long-term dependency, attention mechanism
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Zhang Wei139253.03
Dong Yang221120.38
Hongchao Wang3356.19
Jun Zhang430.74
Mikael Gidlund552352.95