{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:25:31Z","timestamp":1766298331444,"version":"3.41.2"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T00:00:00Z","timestamp":1533081600000},"content-version":"vor","delay-in-days":212,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51575021","51605014"],"award-info":[{"award-number":["51575021","51605014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>Electromechanical actuators (EMAs) are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition (VMD) multifractal detrended fluctuation analysis (MFDFA) and probabilistic neural network (PNN). First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions (IMFs). Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis (PCA) was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network (PNN) was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD\u2010MFDFA method for feature extraction.<\/jats:p>","DOI":"10.1155\/2018\/9154682","type":"journal-article","created":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T23:45:28Z","timestamp":1533167128000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4278-3336","authenticated-orcid":false,"given":"Hongmei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3379-819X","authenticated-orcid":false,"given":"Jiayao","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5932-469X","authenticated-orcid":false,"given":"Jian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010171"},{"key":"e_1_2_10_2_2","first-page":"119","article-title":"Airborne electro-mechanical actuator test stand for development of prognostic health management systems","volume":"32","author":"Balaban E.","year":"2010","journal-title":"Journal of social welfare & Family Law"},{"key":"e_1_2_10_3_2","doi-asserted-by":"crossref","unstructured":"BalabanE. BansalP. StoeltingP. SaxenaA. GoebelK. F. andCurranS. A diagnostic approach for electro-mechanical actuators in aerospace systems 2009 IEEE Aerospace conference March 2009 Big Sky MT USA 1\u201313 https:\/\/doi.org\/10.1109\/AERO.2009.4839661 2-s2.0-70349134011.","DOI":"10.1109\/AERO.2009.4839661"},{"key":"e_1_2_10_4_2","unstructured":"NarasimhanS. RoychoudhuryI. BalabanE. andSaxenaA. Combining model-based and feature-driven diagnosis approaches\u2013a case study on electromechanical actuators 21st International Workshop on Principles of Diagnosis 2010 October 2010 Portland OR USA 1\u20138."},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.21595\/vp.2017.19247"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-7796(02)00035-4"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/18.57199"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/519502"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechmachtheory.2014.01.011"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0164111"},{"key":"e_1_2_10_11_2","first-page":"243","article-title":"Gear fault diagnosis based on narrowband demodulation with frequency shift and spectrum edit","volume":"6","author":"Guo Y.","year":"2016","journal-title":"International Journal of Engineering and Technology Innovation"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"e_1_2_10_13_2","first-page":"299","article-title":"Vibration fault diagnosis of hydropower unit based on multi-fractal spectrum and improved BP neural network","volume":"33","author":"Guo P.","year":"2014","journal-title":"Journal of Hydroelectric Engineering"},{"key":"e_1_2_10_14_2","first-page":"132","article-title":"Fault diagnosis of gearbox based on multi-fractal and PSO-SVM","volume":"39","author":"Li S.","year":"2015","journal-title":"Journal of Mechanical Transmission"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.02.002"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/amm.263-266.108"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.02.020"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2016.02.011"},{"key":"e_1_2_10_19_2","first-page":"3358","article-title":"Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering","volume":"35","author":"Liu C.","year":"2015","journal-title":"Proceedings of the Chinese Society of Electrical Engineering"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2014.09.010"},{"key":"e_1_2_10_21_2","unstructured":"MukhopadhyayS. 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Application of wavelet packet analysis and probabilistic neural networks in fault diagnosis 1 2006 6th World Congress on Intelligent Control and Automation June 2006 Dalian China 4378\u20134381 https:\/\/doi.org\/10.1109\/wcica.2006.1713204 2-s2.0-34047210891.","DOI":"10.1109\/WCICA.2006.1713204"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/9154682.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/9154682.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2018\/9154682","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T22:58:02Z","timestamp":1723157882000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2018\/9154682"}},"subtitle":[],"editor":[{"given":"Minvydas","family":"Ragulskis","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["10.1155\/2018\/9154682"],"URL":"https:\/\/doi.org\/10.1155\/2018\/9154682","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"2018-02-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-06-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9154682"}}