{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T08:27:27Z","timestamp":1768638447448,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&D Program of Zhejiang","award":["2024C01135"],"award-info":[{"award-number":["2024C01135"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Acoustic-based anomaly detection in elevators is crucial for predictive maintenance and operational safety, yet it faces significant challenges in real-world settings, including pervasive multi-source acoustic interference within confined spaces and severe class imbalance in collected data, which critically degrades the detection performance for minority yet critical acoustic events. To address these issues, this study proposes a novel hierarchical self-distillation framework. The method embeds auxiliary classifiers into the intermediate layers of a backbone network, creating a deep teacher\u2013shallow student knowledge transfer paradigm optimized jointly via Kullback\u2013Leibler divergence and feature alignment losses. A self-attentive temporal pooling layer is introduced to adaptively weigh discriminative time-frequency features, thereby mitigating temporal overlap interference, while a focal loss function is employed specifically in the teacher model to recalibrate the learning focus towards hard-to-classify minority samples. Extensive evaluations on the public UrbanSound8K dataset and a proprietary industrial elevator audio dataset demonstrate that the proposed model achieves superior performance, exceeding 90% in both accuracy and F1-score. Notably, it yields substantial improvements in recognizing rare events, validating its robustness for elevator acoustic monitoring.<\/jats:p>","DOI":"10.3390\/s26020589","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T14:35:38Z","timestamp":1768487738000},"page":"589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Self-Distillation with Attention for Class-Imbalanced Acoustic Event Classification in Elevators"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3010-1622","authenticated-orcid":false,"given":"Shengying","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyan","family":"Chou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyang","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC 3000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingsheng","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1109\/TII.2019.2910524","article-title":"Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission","volume":"15","author":"Shevchik","year":"2019","journal-title":"IEEE Trans. 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