To tackle issues including short length,strong technical specificity and challenges in intelligent reuse of signal equipment fault text data,a signal equipment fault text clustering method based on improved Biterm Topic Model and Word Vector Fusion(IBTM-TMW)is proposed.Firstly,to reduce noise of the data and improve data quality,a customized dictionary and gerund processing are introduced in the process of data preprocessing.Secondly,during the Gibbs sampling modeling process of word pairs,the differential importance of words is introduced as a weighting factor,and the Improved Biterm Topic Model(IBTM)is used to improve the learning capability of text topic features.The weight of Term Frequency-Modified Inverse Document Frequency(TF-MIDF)is embedded into the generation process of Word2vec word vectors.The text importance of words is integrated into the Word2vec word vector to refine the feature vector representation of text words.Finally,the text topic feature vector and the word feature vector are integrated to enhance the text feature representation capability.On this basis,the K-means++algorithm is used for fault cluster analysis.The results show that within the same data set,the quality of the text feature vector generated by IBTM-TMW model is significantly higher than those of LDA and Label-LDA models,and its diagnostic accuracy of Correct Classification Rate(CCR)reaches 89.9％(surpassing the 78.3％,68.1％,87.9％and 81.7％accuracies of K-means,GMM,AGNES and BIRCH,respectively).The proposed method improves the capability of analyzing the correlation between fault text features and their categories,thereby offering a valuable reference for text-data-driven fault diagnosis.
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