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dc.contributor.authorZhou, Shenghan
dc.contributor.authorChen, Bang
dc.contributor.authorZhang, Yue
dc.contributor.authorLiu, HouXiang
dc.contributor.authorXiao, Yiyong
dc.contributor.authorPan, Xing
dc.date2020-12
dc.date.accessioned2022-04-05T10:29:29Z
dc.date.available2022-04-05T10:29:29Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12816
dc.description.abstractAs a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text’s context and blindness to the topic of the corpus. This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis. A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered. To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study. The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 4
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2838es_ES
dc.rightsopenAccesses_ES
dc.subjectfeature extractiones_ES
dc.subjectfeature fusiones_ES
dc.subjecttext mininges_ES
dc.subjectfailure diagnosises_ES
dc.subjectIJIMAIes_ES
dc.titleA Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Fieldes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.11.006


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