Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features
- Submitting institution
-
Glasgow Caledonian University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 33608773
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- 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. Since this method is demonstrated to be successful with real field data, it inspired further work towards a developed classification algorithm embedded in a EMI surveyor instrument. This paper attracted an offer of cross-institutional collaboration with Universidad Politécnica de Madrid and the publication of a follow up paper. The citations indicate that this work encouraged other researchers to derive similar algorithms for bearings fault diagnosis, for instance, and PD detection at various power equipment.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -