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

• Main Authors: Wenbing Chang, Zhenzhong Xu, Meng You, Shenghan Zhou, Yiyong Xiao, Yang Cheng
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Entropy
• Subjects: textual data; word2vec; CFSFDP; PrefixSpan; Bayesian failure network
• Online Access: https://www.mdpi.com/1099-4300/20/12/923

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.