{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:09:08Z","timestamp":1784606948416,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51875498"],"award-info":[{"award-number":["51875498"]}]},{"name":"National Natural Science Foundation of China","award":["51475405"],"award-info":[{"award-number":["51475405"]}]},{"name":"National Natural Science Foundation of China","award":["E2018203339"],"award-info":[{"award-number":["E2018203339"]}]},{"name":"National Natural Science Foundation of China","award":["F2020203058"],"award-info":[{"award-number":["F2020203058"]}]},{"name":"Key Project of Natural Science Foundation of Hebei Province, China","award":["51875498"],"award-info":[{"award-number":["51875498"]}]},{"name":"Key Project of Natural Science Foundation of Hebei Province, China","award":["51475405"],"award-info":[{"award-number":["51475405"]}]},{"name":"Key Project of Natural Science Foundation of Hebei Province, China","award":["E2018203339"],"award-info":[{"award-number":["E2018203339"]}]},{"name":"Key Project of Natural Science Foundation of Hebei Province, China","award":["F2020203058"],"award-info":[{"award-number":["F2020203058"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>As a hydraulic pump is the power source of a hydraulic system, predicting its remaining useful life (RUL) can effectively improve the operating efficiency of the hydraulic system and reduce the incidence of failure. This paper presents a scheme for predicting the RUL of a hydraulic pump (gear pump) through a combination of a deep convolutional autoencoder (DCAE) and a bidirectional long short-term memory (Bi-LSTM) network. The vibration data were characterized by the DCAE, and a health indicator (HI) was constructed and modeled to determine the degradation state of the gear pump. The DCAE is a typical symmetric neural network, which can effectively extract characteristics from the data by using the symmetry of the encoding network and decoding network. After processing the original vibration data segment, health indicators were entered as a label into the RUL prediction model based on the Bi-LSTM network, and model training was carried out to achieve the RUL prediction of the gear pump. To verify the validity of the methodology, a gear pump accelerated life experiment was carried out, and whole life cycle data were obtained for method validation. The results show that the constructed HI can effectively characterize the degenerative state of the gear pump, and the proposed RUL prediction method can effectively predict the degeneration trend of the gear pump.<\/jats:p>","DOI":"10.3390\/sym14061111","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"1111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Research on Prediction Method of Gear Pump Remaining Useful Life Based on DCAE and Bi-LSTM"],"prefix":"10.3390","volume":"14","author":[{"given":"Chenyang","family":"Wang","sequence":"first","affiliation":[{"name":"Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China"},{"name":"Key Laboratory of Advanced Forging & Stamping Technology and Science, Ministry of Education of China, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanlu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China"},{"name":"Key Laboratory of Advanced Forging & Stamping Technology and Science, Ministry of Education of China, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yue","sequence":"additional","affiliation":[{"name":"Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China"},{"name":"Key Laboratory of Advanced Forging & Stamping Technology and Science, Ministry of Education of China, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","article-title":"Machinery health prognostics: A systematic review from data acquisition to RUL prediction","volume":"104","author":"Lei","year":"2018","journal-title":"Mech. 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