Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features
- Submitting institution
-
Glasgow Caledonian University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 33608781
- Type
- D - Journal article
- DOI
-
10.3390/s18020406
- Title of journal
- Sensors
- Article number
- 406
- First page
- -
- Volume
- 18
- Issue
- 2
- ISSN
- 1424-8220
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI non-stationary signals classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to decompose the multi-component signal into single frequency component signal and extract entropy features from the single component instead of the compound signal. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -