01910nas a2200265   4500000000100000000000100001008004100002260001200043653002300055653001900078653001600097653002200113100001800135700001400153700001400167700001700181700001600198700001300214245011900227856008100346300001200427490000600439520118500445022001401630       2020                            d  c12/202010aFeature Extraction10aFeature Fusion10aText Mining10aFailure Diagnosis1 aShenghan Zhou1 aBang Chen1 aYue Zhang1 aHouXiang Liu1 aYiyong Xiao1 aXing Pan00aA Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field  uhttps://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_13.pdf  a121-1300 v63 aAs 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.  a1989-1660