{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:22:26Z","timestamp":1771003346459,"version":"3.50.1"},"reference-count":15,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,5,6]]},"abstract":"<jats:p>Aircraft maneuver partition, which dividing flight data into meaningful maneuvers, is an essential preprocess method for health monitoring, flight simulation and flying quality evaluating. Maneuver partition usually needs flight testing and manual interpretation, which is time-consuming, higher cost, and lower versatility. In this paper, a non-supervised automatic method of aircraft maneuver partition (NSAM) is proposed by using data mining without any priori knowledge: Select 6 parameters, height, speed, angle of pitch, angle of bank, angle of yaw, and normal overload; Extract action parts according to the trends of the normal overload, the main parameters; Use the iterative self-organized data analysis algorithm (ISODATA) and divide action parts by numeric features of parameters into classes that represent maneuvers. Applying the NSAM into the small-scale and large-scale data respectively has the results that at least 89% of the maneuvers can be recognized and classified correctly. It indicates that the NSAM is effective and meets the requirements of engineering accuracy.<\/jats:p>","DOI":"10.3233\/jcm-204511","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T11:19:49Z","timestamp":1594725589000},"page":"383-395","source":"Crossref","is-referenced-by-count":1,"title":["A non-supervised automatic method of aircraft maneuver partition"],"prefix":"10.1177","volume":"21","author":[{"given":"Lijie","family":"Zhang","sequence":"first","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/JCM-204511_ref2","doi-asserted-by":"crossref","unstructured":"N.C. Oza, K. Tumer, I.Y. Tumer et al., Classification of aircraft maneuvers for fault detection, in: Multiple Classifier Systems, Springer Berlin Heidelberg, 2003, pp. 375\u2013384.","DOI":"10.1007\/3-540-44938-8_38"},{"issue":"4","key":"10.3233\/JCM-204511_ref3","first-page":"23","article-title":"Establishment of avion inflight maneuver action recognizing knowledge base","volume":"22","author":"Ni","year":"2005","journal-title":"Computer Simulation"},{"issue":"1","key":"10.3233\/JCM-204511_ref5","first-page":"317","article-title":"Maneuver regime recognition development and verification for H-60 structural monitoring","volume":"63","author":"Barndt","year":"2007","journal-title":"Annual Forum Proceedings-American Helicopter Society"},{"key":"10.3233\/JCM-204511_ref6","doi-asserted-by":"crossref","unstructured":"D. He, S. Wu and E. 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