{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T23:22:25Z","timestamp":1783552945931,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T00:00:00Z","timestamp":1597795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["D2181830"],"award-info":[{"award-number":["D2181830"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51675186"],"award-info":[{"award-number":["51675186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSFC-RSE","award":["51911530245"],"award-info":[{"award-number":["51911530245"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.<\/jats:p>","DOI":"10.3390\/s20174657","type":"journal-article","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T09:22:31Z","timestamp":1597828951000},"page":"4657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations"],"prefix":"10.3390","volume":"20","author":[{"given":"Min","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2712-0411","authenticated-orcid":false,"given":"Xifan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wocheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kesai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.asoc.2016.03.013","article-title":"A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction","volume":"44","author":"Liao","year":"2016","journal-title":"Appl. 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