{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:07:00Z","timestamp":1778800020961,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61876059"],"award-info":[{"award-number":["61876059"]}]},{"name":"National Natural Science Foundation of China","award":["61933013"],"award-info":[{"award-number":["61933013"]}]},{"name":"National Natural Science Foundation of China","award":["2021A1515011970"],"award-info":[{"award-number":["2021A1515011970"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["61876059"],"award-info":[{"award-number":["61876059"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["61933013"],"award-info":[{"award-number":["61933013"]}]},{"name":"Natural Science Foundation of Guangdong Province","award":["2021A1515011970"],"award-info":[{"award-number":["2021A1515011970"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Efficient fault diagnosis of rotating machinery is essential for the safe operation of equipment in the manufacturing industry. In this study, a robust and lightweight framework consisting of two lightweight temporal convolutional network (LTCN) backbones and a broad learning system with incremental learning (IBLS) classifier called LTCN-IBLS is proposed for the fault diagnosis of rotating machinery. The two LTCN backbones extract the fault\u2019s time\u2013frequency and temporal features with strict time constraints. The features are fused to obtain more comprehensive and advanced fault information and input into the IBLS classifier. The IBLS classifier is employed to identify the faults and exhibits a strong nonlinear mapping ability. The contributions of the framework\u2019s components are analyzed by ablation experiments. The framework\u2019s performance is verified by comparing it with other state-of-the-art models using four evaluation metrics (accuracy, macro-recall (MR), macro-precision (MP), and macro-F1 score (MF)) and the number of trainable parameters on three datasets. Gaussian white noise is introduced into the datasets to evaluate the robustness of the LTCN-IBLS. The results show that our framework provides the highest mean values of the evaluation metrics (accuracy \u2265 0.9158, MP \u2265 0.9235, MR \u2265 0.9158, and MF \u2265 0.9148) and the lowest number of trainable parameters (\u22640.0165 Mage), indicating its high effectiveness and strong robustness for fault diagnosis.<\/jats:p>","DOI":"10.3390\/s23125642","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T08:56:01Z","timestamp":1686905761000},"page":"5642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System"],"prefix":"10.3390","volume":"23","author":[{"given":"Hao","family":"Wei","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"},{"name":"School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"},{"name":"School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China"},{"name":"Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106625","DOI":"10.1016\/j.ymssp.2020.106625","article-title":"Application of neural network algorithm in fault diagnosis of mechanical intelligence","volume":"141","author":"Xu","year":"2020","journal-title":"Mech. 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