{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:00:09Z","timestamp":1787068809453,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T00:00:00Z","timestamp":1525996800000},"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>Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.<\/jats:p>","DOI":"10.3390\/s18051523","type":"journal-article","created":{"date-parts":[[2018,5,14]],"date-time":"2018-05-14T02:57:20Z","timestamp":1526266640000},"page":"1523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4806-2414","authenticated-orcid":false,"given":"Chang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xihui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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 CD, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5709","DOI":"10.1109\/TIE.2015.2410254","article-title":"Stator current analysis from electrical machines using resonance residual technique to detect faults in planetary gearboxes","volume":"62","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ymssp.2016.02.047","article-title":"Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under nonstationary conditions","volume":"76\u201377","author":"Feng","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1007\/s12206-016-0505-y","article-title":"Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS","volume":"30","author":"Chen","year":"2016","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/TR.2016.2590997","article-title":"Model-based fault diagnosis of a planetary gear: A novel approach using transmission error","volume":"65","author":"Park","year":"2016","journal-title":"IEEE Trans. Reliab."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1016\/j.measurement.2012.10.026","article-title":"Gear fault identification based on Hilbert\u2013Huang transform and SOM neural network","volume":"46","author":"Cheng","year":"2013","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.ymssp.2016.03.007","article-title":"Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines","volume":"81","author":"Singh","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Xu, Y., Luo, M., Li, T., and Song, G. (2017). ECG Signal de-noising and baseline wander correction based on CEEMDAN and wavelet threshold. Sensors, 17.","DOI":"10.3390\/s17122754"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"035102","DOI":"10.1088\/1361-6501\/aa56d3","article-title":"An optimized ensemble local mean decomposition method for fault detection of mechanical components","volume":"28","author":"Zhang","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yan, J., Hong, H., Zhao, H., Li, Y., Gu, C., and Zhu, X. (2016). Through-wall multiple targets vital signs tracking based on VMD algorithm. Sensors, 16.","DOI":"10.3390\/s16081293"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/LGRS.2017.2656158","article-title":"Spectral decomposition for hydrocarbon detection based on VMD and Teager-Kaiser energy","volume":"14","author":"Liu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiao, J., Zhao, M., Lin, J., and Liang, K. (2018). Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings. Reliab. Eng. Syst. Saf., in press.","DOI":"10.1016\/j.ress.2018.02.010"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0888-3270(03)00075-X","article-title":"Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography","volume":"18","author":"Peng","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Caesarendra, W., and Tjahjowidodo, T. (2017). A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines, 5.","DOI":"10.3390\/machines5040021"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jsv.2014.09.039","article-title":"Output-only cyclo-stationary linear-parameter time-varying stochastic subspace identification method for rotating machinery and spinning structures","volume":"337","author":"Velazquez","year":"2015","journal-title":"J. Sound Vib."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.jsv.2015.01.014","article-title":"Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis","volume":"344","author":"Cong","year":"2015","journal-title":"J. Sound Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.ymssp.2017.02.043","article-title":"Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis","volume":"94","author":"Zhang","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.measurement.2016.04.078","article-title":"Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning","volume":"90","author":"Feng","year":"2016","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ymssp.2015.08.012","article-title":"SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults","volume":"70\u201371","author":"Golafshan","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.ymssp.2014.07.019","article-title":"Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis","volume":"52\u201353","author":"Jiang","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.measurement.2016.07.047","article-title":"Automatic damage identification of roller bearings and effects of sifting stop criterion of IMFs","volume":"93","author":"Tabrizi","year":"2016","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.apacoust.2018.03.010","article-title":"Acoustic based fault diagnosis of three-phase induction motor","volume":"133","author":"Glowacz","year":"2018","journal-title":"Appl. Acoust."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.measurement.2016.05.056","article-title":"Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks","volume":"91","author":"Dabrowski","year":"2016","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.measurement.2016.05.059","article-title":"Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition","volume":"91","author":"Cheng","year":"2016","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/TITS.2014.2387069","article-title":"Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy","volume":"16","author":"Huang","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/TNNLS.2015.2506664","article-title":"DISC: Deep image saliency computing via progressive representation learning","volume":"27","author":"Chen","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","first-page":"1334","article-title":"End-to-end training of deep visuomotor policies","volume":"17","author":"Levine","year":"2015","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1523\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:55Z","timestamp":1760195035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,11]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051523"],"URL":"https:\/\/doi.org\/10.3390\/s18051523","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,11]]}}}