{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:12:50Z","timestamp":1771103570142,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,28]],"date-time":"2018-05-28T00:00:00Z","timestamp":1527465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions and Fundamental Research Funds for the Central Universities","award":["2015YC02"],"award-info":[{"award-number":["2015YC02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective.<\/jats:p>","DOI":"10.3390\/s18061735","type":"journal-article","created":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T02:58:18Z","timestamp":1527562698000},"page":"1735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum"],"prefix":"10.3390","volume":"18","author":[{"given":"Yong","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8094-3436","authenticated-orcid":false,"given":"Yusong","family":"Pang","sequence":"additional","affiliation":[{"name":"Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft 2628, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moshen","family":"Kuai","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.measurement.2015.05.007","article-title":"Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance","volume":"73","author":"Chen","year":"2015","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21857","DOI":"10.3390\/s150921857","article-title":"Multi-sensor data fusion using a relevance vector machine based on an ant colony for gearbox fault detection","volume":"15","author":"Liu","year":"2015","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ymssp.2016.02.049","article-title":"A comparative study between empirical wavelet transforms and empirical mode decomposition methods: Application to bearing defect diagnosis","volume":"81","author":"Kedadouche","year":"2016","journal-title":"Mech. 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