{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:12:52Z","timestamp":1773796372129,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T00:00:00Z","timestamp":1605657600000},"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":["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"]}]},{"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>A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.<\/jats:p>","DOI":"10.3390\/s20226576","type":"journal-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T07:41:00Z","timestamp":1605685260000},"page":"6576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump"],"prefix":"10.3390","volume":"20","author":[{"given":"Shengnan","family":"Tang","sequence":"first","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Shouqi","family":"Yuan","sequence":"additional","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":"Guangpeng","family":"Li","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.ymssp.2012.10.020","article-title":"Layered clustering multi-fault diagnosis for hydraulic piston pump","volume":"36","author":"Du","year":"2013","journal-title":"Mech. 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