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Irfan, Muhammad, Alwadie, Abdullah Saeed, Glowacz, Adam, Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Rahman, Saifur, Khan, Mohammad Kamal Asif, Jalalah, Mohammed, Alshorman, Omar and Caesarendra, Wahyu
(2021)
A novel feature extraction and fault detection technique for the intelligent fault identification of water pump bearings.
Sensors, 21 (12).
ISSN 1424-8220
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Abstract
The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: The authors would like to acknowledge the support of the Deputy for Research and Innovation of the Ministry of Education, Kingdom of Saudi Arabia, for this research through a grant (NU/IFC/ENT/01/011) under the institutional Funding Committee at Najran University, Kingdom of Saudi Arabia. |
| Uncontrolled Keywords: | feature selection,induction motors,instantaneous power measurement,stator current sensing,vibration measurement,voltage measurement,analytical chemistry,information systems,atomic and molecular physics, and optics,biochemistry,instrumentation,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1600/1602 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Oct 2023 00:44 |
| Last Modified: | 10 Dec 2024 01:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93304 |
| DOI: | 10.3390/s21124225 |
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