Principal fault localization of the faults chain, as a branch of fault diagnosis in wind turbine system, has been an essential problem to ensure the reliability and security in the real wind farms recently. It can be solved by machine learning techniques with historical data labeled with principal faults. However, most real data are unlabeled, since the labeled is expensive to obtain, which increases the difficulty to localize the principal fault if just using unlabeled data and few labeled data. So, in this article, a novel approach using unlabeled data is proposed for principal fault localization of the faults chain in wind turbine systems. First, a deep learning model, stacked sparse autoencoders, is introduced to learn and extract high-level features from data. Then, we present a graphical temporal semi-supervised learning algorithm to develop the pseudo-labeled data set with an unlabeled data set. Considering the temporal correlation of wind power data, we add a time weight vector and apply the cosine-similarity in the proposed algorithm. Finally, based on the pseudo-labeled data set, a classifier model is built and trained for the principal fault localization of the faults chain. The proposed approach is verified by the real buffer data set collected from two wind farms in China, and the experimental results show its effectiveness in practice.

Lnc-RAB11B-AS1 is reported to be dysregulated in several types of cancers and can function as both an oncogene and tumor suppressor gene. To evaluate the potential role of lnc-RAB11B-AS1 in hepatocellular carcinoma (HCC), we investigated and evaluated its expression in HCC based on the data mining of a series of public databases, including TCGA, GEO, ICGC, HPA, DAVID, cBioPortal, GeneMIANA, TIMER, and ENCORI. The data showed downregulation of lnc-RAB11B-AS1 in HCC and was accompanied by the synchronous downregulation of the targeted RAB11B mRNA and its protein. Low expression of lnc-RAB11B-AS1 was associated with shorter overall survival (OS) and disease-free survival (DFS) of HCC patients, PD1/PD-L1 was correlated with low expression of RAB11B. Furthermore, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed a correlation between immune cell change and non-alcoholic fatty liver disease. The above findings revealed that lnc-RAB11B-AS1 was down-regulated in HCC and closely associated with the clinical stage of the HCC patients, suggesting that lnc-RAB11B-AS1 could be a possible predictor for HCC and a potential new therapeutic target for the treatment of HCC.

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