{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:59:00Z","timestamp":1773777540606,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T00:00:00Z","timestamp":1557187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Basic Research Program of China (973 Program)","award":["2016YFB1200402"],"award-info":[{"award-number":["2016YFB1200402"]}]},{"name":"Fundamental Research Funds for the Central University","award":["2018RC009"],"award-info":[{"award-number":["2018RC009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Aiming at fault feature extraction of a hydraulic pump signal, a new method based on symplectic geometry mode decomposition (SGMD) and power spectral entropy (PSE) is proposed. First, the SGMD is applied to decompose a multi-component fault signal, then the N symplectic geometry components (SGCs) can be obtained. Second, the N SGCs are reconstructed as a signal of interest and, consequently, the power spectral entropy of each constructed signal is computed to quantify the complexity and uncertainty of their spectra. Finally, the difference value (D-value) between the adjacent entropies is used as a SGCs criterion, whose turning point indicates the most information of reconstructed signal. Hydraulic pump signals are tested and verified, and results demonstrate that the proposed method can extract the richest fault feature information of hydraulic pump signals effectively.<\/jats:p>","DOI":"10.3390\/e21050476","type":"journal-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T11:00:57Z","timestamp":1557745257000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy"],"prefix":"10.3390","volume":"21","author":[{"given":"Zhi","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3308-0014","authenticated-orcid":false,"given":"Ge","family":"Xin","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.measurement.2018.03.050","article-title":"Fault diagnosis on slipper abrasion of axial piston pump based on extreme learning machine","volume":"124","author":"Lan","year":"2018","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1109\/JSEN.2018.2804908","article-title":"Cyclic Spectral Analysis of vibration signals for centrifugal pump fault characterization","volume":"19","author":"Sun","year":"2018","journal-title":"IEEE Sens. 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