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We propose the jointly optimized semi-supervised graph-embedded stochastic configuration network (JOSGESCN). The method constructs geometric similarity between labelled and unlabelled samples and injects this structure into the model via manifold regularization, enabling effective use of unlabelled data. We further introduce a supervisory mechanism that adaptively configures hidden nodes to strengthen representation capacity. A joint optimization strategy reduces error propagation from one-shot pseudo-labelling and encourages low-density separation of the decision boundary. We evaluate JOSGESCN on three industrial fault-diagnosis data sets against support vector machine (SVM), random vector functional link network (RVFL), stochastic configuration network (SCN), semi-supervised random vector functional link (SSRVFL), jointly optimized semi-supervised random vector functional link (JOSRVFL), and LPSCN (locality-preserving stochastic configuration network). Across all data sets, JOSGESCN delivers superior accuracy, demonstrating consistent gains over strong baselines while maintaining practical training efficiency. These results indicate that coupling graph-based manifold priors with adaptive stochastic configuration is a promising direction for label-efficient, high-performance industrial health monitoring.<\/jats:p>","DOI":"10.1093\/jcde\/qwag017","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T12:34:03Z","timestamp":1772368443000},"page":"246-261","source":"Crossref","is-referenced-by-count":0,"title":["Jointly optimized semi-supervised graph embedding stochastic configuration networks for industrial fault diagnosis"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0227-020X","authenticated-orcid":false,"given":"Panliang","family":"Yuan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 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