TY - JOUR
T1 - Product quality reliability analysis based on rough Bayesian network
AU - Zhang, Wanjuan
AU - Wang, Xiaodan
AU - Cabrera, Diego
AU - Bai, Yun
N1 - Publisher Copyright:
© 2020 Totem Publisher, Inc.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Simultaneous quality reliability analysis can detect the weak links in production process as early as possible, which can significantly improve product reliability. Aiming at the reliability in product quality, a model based on rough set and Bayesian network (RS-BN) is proposed in this paper. Simplify expert knowledge and reduce product quality factors using rough set theory, and the minimal product quality rules can be obtained. Then the Bayesian network is constructed and trained by the minimum rules. Based on the minimal rules, the complexity of Bayesian network structure and the difficulties of product reliability analysis are largely decreased. To verify the performance of the proposed RS-BN model, a competition dataset is utilized and four evaluation indicators are investigated, i.e., accuracy, F1-score, recall, and precision. Experimental results indicated that the proposed model is superior to the other three comparative models.
AB - Simultaneous quality reliability analysis can detect the weak links in production process as early as possible, which can significantly improve product reliability. Aiming at the reliability in product quality, a model based on rough set and Bayesian network (RS-BN) is proposed in this paper. Simplify expert knowledge and reduce product quality factors using rough set theory, and the minimal product quality rules can be obtained. Then the Bayesian network is constructed and trained by the minimum rules. Based on the minimal rules, the complexity of Bayesian network structure and the difficulties of product reliability analysis are largely decreased. To verify the performance of the proposed RS-BN model, a competition dataset is utilized and four evaluation indicators are investigated, i.e., accuracy, F1-score, recall, and precision. Experimental results indicated that the proposed model is superior to the other three comparative models.
KW - Bayesian network
KW - Minimum rules
KW - Product quality
KW - Reliability analysis
KW - Rough set
UR - http://www.scopus.com/inward/record.url?scp=85080083061&partnerID=8YFLogxK
U2 - 10.23940/ijpe.20.01.p5.3747
DO - 10.23940/ijpe.20.01.p5.3747
M3 - Article
AN - SCOPUS:85080083061
SN - 0973-1318
VL - 16
SP - 37
EP - 47
JO - International Journal of Performability Engineering
JF - International Journal of Performability Engineering
IS - 1
ER -