{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:15:01Z","timestamp":1760328901008,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["NRF-2021R1A6A1A03039493","NRF-2022R1F1A1076089"],"award-info":[{"award-number":["NRF-2021R1A6A1A03039493","NRF-2022R1F1A1076089"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two important elements of fault diagnosis using machine learning are data preprocessing and model structure. Multi\u2013class classification is used to classify faults into different single types, whereas multi\u2013label classification classifies faults into compound types. It is valuable to focus on the capability of detecting compound faults because multiple faults can exist simultaneously. Diagnosis of untrained compound faults is also a merit. In this study, input data were first preprocessed with short\u2013time Fourier transform. Then, a model was built for classification of the state of the system based on multi\u2013output classification. Finally, the proposed model was evaluated based on its performance and robustness for classification of compound faults. This study proposes an effective model based on multi\u2013output classification, which can be trained using only single fault data for the classification of compound faults and confirms the robustness of the model to changes in unbalance.<\/jats:p>","DOI":"10.3390\/s23063153","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T03:14:35Z","timestamp":1678936475000},"page":"3153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi\u2013Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non\u2013Contact Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6046-8110","authenticated-orcid":false,"given":"Taehwan","family":"Son","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8553-4276","authenticated-orcid":false,"given":"Dongwoo","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1659-9858","authenticated-orcid":false,"given":"Byeongil","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ling, T., and He, Y. 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