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Entropy 2018, 20(12), 923; https://doi.org/10.3390/e20120923

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

1
School of Reliability and System Engineering, Beihang University, Beijing 100191, China
2
Center for Industrial Production, Aalborg University, 9220 Aalborg, Denmark
These authors contributed equally to this work and should be considered co-first authors.
*
Author to whom correspondence should be addressed.
Received: 23 October 2018 / Revised: 27 November 2018 / Accepted: 30 November 2018 / Published: 3 December 2018
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Abstract

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. View Full-Text
Keywords: textual data; word2vec; CFSFDP; PrefixSpan; Bayesian failure network textual data; word2vec; CFSFDP; PrefixSpan; Bayesian failure network
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Chang, W.; Xu, Z.; You, M.; Zhou, S.; Xiao, Y.; Cheng, Y. A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering. Entropy 2018, 20, 923.

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