MyJournals Home  

RSS FeedsEntropy, Vol. 20, Pages 923: A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering (Entropy)

 
 

8 december 2018 18:00:27

 
Entropy, Vol. 20, Pages 923: A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering (Entropy)
 




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.


Del.icio.us Digg Facebook Google StumbleUpon Twitter
 
24 viewsCategory: Informatics, Physics
 
Entropy, Vol. 20, Pages 924: Effect of Binding and Dispersion Behavior of High-Entropy Alloy (HEA) Powders on the Microstructure and Mechanical Properties in a Novel HEA/Diamond Composite (Entropy)
Entropy, Vol. 20, Pages 922: Computational Simulation of Entropy Generation in a Combustion Chamber Using a Single Burner (Entropy)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics

Use these buttons to bookmark us:
Del.icio.us Digg Facebook Google StumbleUpon Twitter


Valid HTML 4.01 Transitional
Copyright © 2008 - 2018 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Travel Photos Nachrichten Indigonet Finances Leer Mandarijn