A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering

Wenbing Chang, Zhenzhong Xu, Meng You, Shenghan Zhou, Yiyong Xiao, Cheng Yang

Research output: Contribution to journalJournal articleResearchpeer-review

3 Citations (Scopus)
147 Downloads (Pure)


The purpose of this paper is to predict failures based on textual sequence data. The current failure prediction is mainly based on structured data. However, there are many unstructured data in aircraft maintenance. The failure mentioned here refers to failure types, such as transmitter failure and signal failure, which are classified by the clustering algorithm based on the failure text. For the failure text, this paper uses the natural language processing technology. Firstly, segmentation and the removal of stop words for Chinese failure text data is performed. The study applies the word2vec moving distance model to obtain the failure occurrence sequence for failure texts collected in a fixed period of time. According to the distance, a clustering algorithm is used to obtain a typical number of fault types. Secondly, the failure occurrence sequence is mined using sequence mining algorithms, such as-PrefixSpan. Finally, the above failure sequence is used to train the Bayesian failure network model. The final experimental results show that the Bayesian failure network has higher accuracy for failure prediction.

Original languageEnglish
Article number923
Issue number12
Publication statusPublished - 3 Dec 2018


  • Bayesian failure network
  • PrefixSpan
  • Textual data
  • Word2vec

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