A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing
Increased attention has been paid to research on intelligent fault diagnosis under acoustic signals. However, the signal-to-noise ratio of acoustic signals is much lower than vibration signals, which increases the difficulty of signal denoising and feature extraction. To solve the above defect, a no...
Saved in:
Main Authors: | Guowei Zhang, Jinrui Wang, Baokun Han, Sixiang Jia, Xiaoyu Wang, Jingtao He |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2020/8837047 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty
by: Baokun Han, et al.
Published: (2020-01-01) -
Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features
by: Wei Peng, et al.
Published: (2016-01-01) -
FAULT DIAGNOSIS OF RECIPROCATING COMPRESSOR ON THE RESONANCE-BASED SPARSE SIGNAL DECOMPOSITION WITH OPTIMAL Q-FACTOR
by: WANG JinDong, et al.
Published: (2019-01-01) -
Gear Fault Diagnosis based on Feature Fusion and Sparse Representation
by: Wang Jiangping, et al.
Published: (2017-01-01) -
Intelligent Fault Diagnosis Based on Vibration Signal Analysis
by: Minvydas Ragulskis, et al.
Published: (2017-01-01)