Next Article in Journal
Effect of Binding and Dispersion Behavior of High-Entropy Alloy (HEA) Powders on the Microstructure and Mechanical Properties in a Novel HEA/Diamond Composite
Next Article in Special Issue
Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine
Previous Article in Journal
Computational Simulation of Entropy Generation in a Combustion Chamber Using a Single Burner
Previous Article in Special Issue
Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Entropy 2018, 20(12), 923;

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

School of Reliability and System Engineering, Beihang University, Beijing 100191, China
Center for Industrial Production, Aalborg University, 9220 Aalborg, Denmark
Author to whom correspondence should be addressed.
These authors contributed equally to this work and should be considered co-first authors.
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)
Full-Text   |   PDF [1747 KB, uploaded 3 December 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top