{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T06:41:59Z","timestamp":1776580919505,"version":"3.51.2"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks\u2019 ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.<\/jats:p>","DOI":"10.3390\/s24030941","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:44:24Z","timestamp":1706697864000},"page":"941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2506-236X","authenticated-orcid":false,"given":"Dongyan","family":"Miao","sequence":"first","affiliation":[{"name":"State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2214-2216","authenticated-orcid":false,"given":"Kun","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1376-5881","authenticated-orcid":false,"given":"Yuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouzheng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinji","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fentaye, A.D., Baheta, A.T., Gilani, S.I., and Kyprianidis, K.G. 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