{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:59:07Z","timestamp":1775066347856,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2001201,61876055"],"award-info":[{"award-number":["U2001201,61876055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts\u2019 empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model\u2014the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model\u2019s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model\u2014TSK-BLS, achieved significant improvements.<\/jats:p>","DOI":"10.3390\/sym15010083","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T08:42:22Z","timestamp":1672216942000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5294-1599","authenticated-orcid":false,"given":"Xiaojia","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence and Data Science, School of Management, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Cunjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Software Systems and Engineering, School of Software, Tsinghua University, Beijing 100084, China"}]},{"given":"Keyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Data Science, School of Management, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Xibin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Software Systems and Engineering, School of Software, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xiao, N. 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