{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:27:13Z","timestamp":1770888433693,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T00:00:00Z","timestamp":1624147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deputy for Research and Innovation- Ministry of Education, Kingdom of Saudi Arabia","award":["NU\/IFC\/ENT\/01\/011"],"award-info":[{"award-number":["NU\/IFC\/ENT\/01\/011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21124225","type":"journal-article","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T21:50:15Z","timestamp":1624225815000},"page":"4225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-6875","authenticated-orcid":false,"given":"Muhammad","family":"Irfan","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0929-8221","authenticated-orcid":false,"given":"Abdullah Saeed","family":"Alwadie","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0546-7083","authenticated-orcid":false,"given":"Adam","family":"Glowacz","sequence":"additional","affiliation":[{"name":"Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6421-9245","authenticated-orcid":false,"given":"Muhammad","family":"Awais","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Edge Hill University, St Helens Road, Ormskirk L39 4QP, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7262-183X","authenticated-orcid":false,"given":"Saifur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}]},{"given":"Mohammad Kamal Asif","family":"Khan","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6828-3874","authenticated-orcid":false,"given":"Mohammad","family":"Jalalah","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7143-6085","authenticated-orcid":false,"given":"Omar","family":"Alshorman","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9784-4204","authenticated-orcid":false,"given":"Wahyu","family":"Caesarendra","sequence":"additional","affiliation":[{"name":"Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9728","DOI":"10.1109\/TIE.2018.2821645","article-title":"Detection of generalized-roughness and single point bearing fault using linear prediction-based current noise cancellation","volume":"65","author":"Dalvand","year":"2018","journal-title":"IEEE Trans. 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