{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:36:06Z","timestamp":1774542966166,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests.<\/jats:p>","DOI":"10.3390\/s24082470","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T03:34:37Z","timestamp":1712892877000},"page":"2470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Smart Sensor-Based Monitoring Technology for Machinery Fault Detection"],"prefix":"10.3390","volume":"24","author":[{"given":"Ming","family":"Zhang","sequence":"first","affiliation":[{"name":"Automotive Engineering Department, Weifang Institute of Engineering, Qingzhou 262501, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1349-1350","authenticated-orcid":false,"given":"Xing","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3304-0776","authenticated-orcid":false,"given":"Wilson","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MIM.2021.9436098","article-title":"Analysis of fault detection in rolling element bearings","volume":"24","author":"Wang","year":"2021","journal-title":"IEEE Instrum. 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