E-resources
Peer reviewed
-
Cao, Zheng; Dai, Jisheng; Xu, Weichao; Chang, Chunqi
IEEE transactions on instrumentation and measurement, 2022, Volume: 71Journal Article
Extracting fault frequencies from noisy vibration signal is a challenging task for bearing fault diagnosis. The state-of-the-art sparse representation (SR)-based methods usually consist of two steps: 1) fault impulse recovery in the time domain and 2) frequency transformation of the estimated signal envelope. However, any inaccurate time-domain signal recovery can cause an error accumulation problem for the following frequency transformation, and the frequency transformation itself encounters a low-resolution shortcoming especially for short-time sampling data. To handle these shortcomings, in this article, we propose a novel sparse Bayesian learning (SBL) framework to evade the time-domain signal recovery and extract the fault frequencies directly from the frequency domain. We first present a new formulation for the sparse frequency recovery problem using the sparsity structure of the envelope spectrum, and then introduce a truncated off-grid model into the SBL framework to speed up the proposed method. Moreover, an improved grid refinement is developed to jointly combat the off-grid frequency mismatch and exploit the arithmetic sparsity structure of fault frequencies. Both the simulation and experimental results indicate the effectiveness of our proposed method.
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.