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A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering
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 s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512510/ https://www.ncbi.nlm.nih.gov/pubmed/33266647 http://dx.doi.org/10.3390/e20120923 |
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author | Chang, Wenbing Xu, Zhenzhong You, Meng Zhou, Shenghan Xiao, Yiyong Cheng, Yang |
author_facet | Chang, Wenbing Xu, Zhenzhong You, Meng Zhou, Shenghan Xiao, Yiyong Cheng, Yang |
author_sort | Chang, Wenbing |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7512510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75125102020-11-09 A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering Chang, Wenbing Xu, Zhenzhong You, Meng Zhou, Shenghan Xiao, Yiyong Cheng, Yang Entropy (Basel) Article 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. MDPI 2018-12-03 /pmc/articles/PMC7512510/ /pubmed/33266647 http://dx.doi.org/10.3390/e20120923 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chang, Wenbing Xu, Zhenzhong You, Meng Zhou, Shenghan Xiao, Yiyong Cheng, Yang A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering |
title | A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering |
title_full | A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering |
title_fullStr | A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering |
title_full_unstemmed | A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering |
title_short | A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering |
title_sort | bayesian failure prediction network based on text sequence mining and clustering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512510/ https://www.ncbi.nlm.nih.gov/pubmed/33266647 http://dx.doi.org/10.3390/e20120923 |
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