{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:08:54Z","timestamp":1779289734379,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Support Plan for High Level Talents in Zhejiang Province","award":["2021R52036"],"award-info":[{"award-number":["2021R52036"]}]},{"name":"Special Support Plan for High Level Talents in Zhejiang Province","award":["52075496"],"award-info":[{"award-number":["52075496"]}]},{"name":"Special Support Plan for High Level Talents in Zhejiang Province","award":["51505430"],"award-info":[{"award-number":["51505430"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021R52036"],"award-info":[{"award-number":["2021R52036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075496"],"award-info":[{"award-number":["52075496"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51505430"],"award-info":[{"award-number":["51505430"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.<\/jats:p>","DOI":"10.3390\/s24072135","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T12:41:57Z","timestamp":1711543317000},"page":"2135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0022-922X","authenticated-orcid":false,"given":"Jun","family":"Pan","sequence":"first","affiliation":[{"name":"Zhejiang Province\u2019s Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Changxu","family":"Shao","sequence":"additional","affiliation":[{"name":"Zhejiang Province\u2019s Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Yuefang","family":"Dai","sequence":"additional","affiliation":[{"name":"Hangzhou Xizi Iparking Co., Ltd., Hangzhou 311103, China"}]},{"given":"Yimin","family":"Wei","sequence":"additional","affiliation":[{"name":"Zhejiang Province\u2019s Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Wenhua","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Province\u2019s Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Zheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Zhejiang Province\u2019s Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012028","DOI":"10.1088\/1757-899X\/408\/1\/012028","article-title":"MCU System-based Intelligent High-speed Elevator Door Operator Fault Analysis and Research","volume":"428","author":"Wang","year":"2018","journal-title":"IOP Conf. 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