{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:55:56Z","timestamp":1760241356992,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,11]],"date-time":"2018-02-11T00:00:00Z","timestamp":1518307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFF0203804"],"award-info":[{"award-number":["2016YFF0203804"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51775037"],"award-info":[{"award-number":["51775037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["FRF-TP-15-010A3"],"award-info":[{"award-number":["FRF-TP-15-010A3"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM) model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.<\/jats:p>","DOI":"10.3390\/a11020021","type":"journal-article","created":{"date-parts":[[2018,2,12]],"date-time":"2018-02-12T10:50:38Z","timestamp":1518432638000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5273-6867","authenticated-orcid":false,"given":"Lijun","family":"Zhang","sequence":"first","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyu","family":"Tao","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"973","DOI":"10.3390\/algor2030973","article-title":"Advances in artificial neural networks\u2014Methodological development and application","volume":"2","author":"Huang","year":"2009","journal-title":"Algorithms"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.measurement.2017.07.030","article-title":"The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings","volume":"111","author":"Rai","year":"2017","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1049\/iet-smt.2015.0026","article-title":"Bearing fault classification using ANN-based Hilbert footprint analysis","volume":"9","author":"Dubey","year":"2015","journal-title":"IET Sci. 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