{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T13:04:56Z","timestamp":1769000696902,"version":"3.49.0"},"reference-count":11,"publisher":"American Institute of Aeronautics and Astronautics (AIAA)","issue":"11","funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61802176"],"award-info":[{"award-number":["61802176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["arc.aiaa.org"],"crossmark-restriction":true},"short-container-title":["Journal of Aerospace Information Systems"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:p> It is vital and essential to accurately and timely detect various damages of aircraft engines in civil aviation. Currently, aircraft engines are manually inspected via borescope images by aircraft maintenance technicians. This process is time-consuming and prone to error due to human factors. The aim of this paper is to automate the aircraft engine inspection, and this work presents a deep learning framework with a context encoder neural network structure such that the damaged structures can be accurately segmented from borescope images. Moreover, the proposed network structure is further optimized through an orthogonal-array-based method. With the real borescope images collected from a commercial airline company, the proposed framework is compared with existing deep-learning-based methods from various aspects. The experimental results validate that various damages can be automatically detected and recognized with high accuracy and efficiency by the proposed solution. <\/jats:p>","DOI":"10.2514\/1.i010960","type":"journal-article","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T08:37:16Z","timestamp":1632559036000},"page":"803-812","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent Damage Detection for Aircraft Engine with Context Encoder Neural Networks"],"prefix":"10.2514","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1158-3366","authenticated-orcid":false,"given":"Xinjie","family":"Guan","sequence":"first","affiliation":[{"name":"Nanjing Tech University, 211800 Nanjing, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Jian","sequence":"additional","affiliation":[{"name":"Nanjing Tech University, 211800 Nanjing, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xili","family":"Wan","sequence":"additional","affiliation":[{"name":"Nanjing Tech University, 211800 Nanjing, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0329-0480","authenticated-orcid":false,"given":"Renrui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Nanjing Tech University, 211800 Nanjing, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing Tech University, 211800 Nanjing, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1387","reference":[{"key":"r2","unstructured":"DruryC. G. \u201cHuman Factors in Aircraft Inspection,\u201d Tech. Rept. ADP010770, Dept. of Industrial Engineering, State Univ. of New York at Buffalo, 2001, https:\/\/apps.dtic.mil\/sti\/pdfs\/ADP010770.pdf."},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-8141(99)00063-3"},{"key":"r4","volume-title":"Human Factors Good Practices in Borescope Inspection","volume":"8","author":"Drury C. G.","year":"2002"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace7120171"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace6050058"},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.3390\/app9183781"},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12263"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2764844"},{"key":"r19","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903562"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"r25","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2000.10485983"}],"container-title":["Journal of Aerospace Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/arc.aiaa.org\/doi\/pdf\/10.2514\/1.I010960","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T17:24:06Z","timestamp":1689355446000},"score":1,"resource":{"primary":{"URL":"https:\/\/arc.aiaa.org\/doi\/10.2514\/1.I010960"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":11,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10.2514\/1.I010960"],"URL":"https:\/\/doi.org\/10.2514\/1.i010960","relation":{},"ISSN":["1940-3151","2327-3097"],"issn-type":[{"value":"1940-3151","type":"print"},{"value":"2327-3097","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"2021-01-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-05","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}