{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:32:32Z","timestamp":1760149952315,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Jiangsu Province, China","award":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"],"award-info":[{"award-number":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"],"award-info":[{"award-number":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Project of Xuzhou City, China","award":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"],"award-info":[{"award-number":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"]}]},{"name":"National key research and development program, China","award":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"],"award-info":[{"award-number":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"]}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"],"award-info":[{"award-number":["BK20221117","2023M732967","KC22001","2022YFC3005902","52304271"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling\u2019s T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.<\/jats:p>","DOI":"10.3390\/s23198096","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T03:49:14Z","timestamp":1695786554000},"page":"8096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9762-2644","authenticated-orcid":false,"given":"Jinxin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Shenglei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Enyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Jiyun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1733-4712","authenticated-orcid":false,"given":"Xiaofei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Zhonghui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111042","DOI":"10.1016\/j.measurement.2022.111042","article-title":"Energy features fusion based hydraulic cylinder seal wear and internal leakage fault diagnosis method","volume":"195","author":"Qiu","year":"2022","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106300","DOI":"10.1016\/j.engfailanal.2022.106300","article-title":"An adaptive deep learning model towards fault diagnosis of hydraulic piston pump using pressure signal","volume":"138","author":"Tang","year":"2022","journal-title":"Eng. 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