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Firstly, the vibration signals are constructed as a three\u2010way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor\u2010aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.<\/jats:p>","DOI":"10.1155\/2021\/6205890","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T03:50:30Z","timestamp":1639453830000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Chaofan","family":"Hu","sequence":"first","affiliation":[]},{"given":"Zhichao","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Biao","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1153-808X","authenticated-orcid":false,"given":"WeiGuang","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0139-0461","authenticated-orcid":false,"given":"Shuilong","family":"He","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"e_1_2_9_1_2","article-title":"Modified deep auto-encoder driven by multi-source parameters for fault transfer prognosis of aero-engine","author":"He Z.","year":"2021","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3077995"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-020-01663-1"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-019-04689-9"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.12.012"},{"key":"e_1_2_9_6_2","doi-asserted-by":"crossref","unstructured":"WangQ. 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