{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:04:42Z","timestamp":1772035482641,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:00:00Z","timestamp":1607904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51779107"],"award-info":[{"award-number":["51779107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51805214"],"award-info":[{"award-number":["51805214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFC1512402"],"award-info":[{"award-number":["2020YFC1512402"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB2005204"],"award-info":[{"award-number":["2019YFB2005204"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M651722"],"award-info":[{"award-number":["2019M651722"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Science Foundation of Zhejiang Province","award":["ZJ2020090"],"award-info":[{"award-number":["ZJ2020090"]}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation","doi-asserted-by":"publisher","award":["202003N4034"],"award-info":[{"award-number":["202003N4034"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems","award":["GZKF-201905"],"award-info":[{"award-number":["GZKF-201905"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20170548"],"award-info":[{"award-number":["BK20170548"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.<\/jats:p>","DOI":"10.3390\/s20247152","type":"journal-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:45:36Z","timestamp":1607906736000},"page":"7152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Shengnan","family":"Tang","sequence":"first","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6217-088X","authenticated-orcid":false,"given":"Yong","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"},{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"},{"name":"Ningbo Academy of Product and Food Quality Inspection, Ningbo 315048, China"}]},{"given":"Shouqi","family":"Yuan","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Guangpeng","family":"Li","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112413","DOI":"10.1016\/j.enconman.2019.112413","article-title":"The applications of energy regeneration and conversion technologies based on hydraulic transmission systems: A review","volume":"205","author":"He","year":"2020","journal-title":"Energy Convers. 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