Paper Info
Title
Construction of a Hierarchical Feature Enhancement Network and Its Application in Fault Recognition
Abstract
Industrial Internet of Things (IIoT) provide significant support for observing and controlling industrial machinery. In this article, a novel hierarchical feature enhancement network (HFEN) is proposed by combining signal processing and representation learning. The signal processing block extracts features with definite physical significance. Then, the representability of the physical features is improved by connecting stacked denoising autoencoders and squeeze-and-excitation networks. A novel two-stream architecture is designed for HFEN to fuse two types of features. Consequently, HFEN can extract features that can be analyzed for physical significance and that are also representative in terms of recognizable patterns. The experimental results prove that the performance of HFEN is satisfactory in terms of accuracy and efficiency when compared to other methods. Finally, this article also aims to demonstrate the potential of a new pairing that fuses the model- and data-driven strategies for IIoT.
Year
DOI
Venue
2021
10.1109/TII.2020.3021688
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Feature extraction,Vibrations,Machine learning,Signal processing,Wavelet transforms,Time-frequency analysis,Informatics
Journal
17
Issue
ISSN
Citations 
7
1551-3203
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Zhe Chen164.20
Huimin Lu278073.60
Shiqing Tian310.35
Junlin Qiu421.38
Tohru Kamiya510.35
Seiichi Serikawa654038.54
Xu Lizhong715524.51