Abstract
Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree (FPT) of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth (FPG) algorithm. Then on the basis of fuzzy Bayesian network (FBN) and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
ZHEN X, LI J D. Established method study for superparameter of prior distribution in reliability test [J]. Electronic Product Reliability and Environmental Testing, 2013, 31(5): 14–16 (in Chinese).
ZHOU H, ZHU Y L. Research on selection and determination of RMS parameters of military engineer machinery [J]. Engineer Equipment Research, 2011, 30(1): 53–57 (in Chinese).
LU X Q, JIN L. Selection of heat-setting parameters of mechanical products with DOE method [J]. Materials and Heat Treatment Technology, 2012, 41(16): 216–217 (in Chinese).
SHAO L C, ZHU J G, JI J C, et al. Reliability parameters’ selection when a decoy erecting [J]. Engineer Equipment Research, 2011, 30(5): 9–12 (in Chinese).
SHUBHADA P N, RAJENDRA K. Text mining [J]. Library Review, 2015, 64(3): 248–268.
LIAO Q, HAO Z F, CHEN Z H. Data mining and mathematics model [M]. Beijing: National Defense Industry Press, 2010 (in Chinese).
ZHAO J L, ZHENG W. Study of fault diagnosis method based on fuzzy Bayesian network and application in CTCS-3 train control system [C]//2013 IEEE International Conference on Intelligent Rail Transportation. [s.l.]: IEEE, 2013: 249–254.
DONALD J B, DAMES A M, DEZON K F, et al. A case study of data quality in text mining clinical progress notes [J]. ACM Transaction on Management Information System, 2015, 6(1): 1–21.
WANG W J, LIU B X. Association rule-based network intrusion detection system [J]. Nuclear Electronics and Detection Technology, 2015, 35(2): 119–123 (in Chinese).
NADERPOUR M, LU J, ZHANG G Q. A fuzzy dynamic Bayesian network-based situation assessment approach [C]//2013 IEEE International Conference on Fuzzy Systems. [s.l.]: IEEE, 2013: 1–8.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: the Weapon Equipment Beforehand Research Foundation of China (No. 9140A19030314JB35275), and the Army Technology Element Foundation of China (No. A157167)
Rights and permissions
About this article
Cite this article
Shuai, Y., Song, T., Wang, J. et al. Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth Algorithm and Fuzzy Bayesian Network. J. Shanghai Jiaotong Univ. (Sci.) 23, 423–428 (2018). https://doi.org/10.1007/s12204-018-1945-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12204-018-1945-6