Research on Bayesian fault diagnosis model of traditional Chinese medicine dry granulation based on failure model and effect analysis (FMEA).
10.19540/j.cnki.cjcmm.20200911.311
- Author:
Di GAO
;
Ya-Jing WANG
;
Yan-Wen WANG
;
Xiang-Yin YE
;
Yu WANG
;
Xiao-Yu WANG
;
Zan-Yang HUANG
- Publication Type:Journal Article
- Keywords:
Bayesian network;
dry granulation;
failure mode and effect analysis;
fault diagnosis;
particle quality;
reasoning verification;
traditional Chinese medicine
- MeSH:
Bayes Theorem;
Medicine, Chinese Traditional;
Probability
- From:
China Journal of Chinese Materia Medica
2020;45(24):5982-5987
- CountryChina
- Language:Chinese
-
Abstract:
This paper aims to construct a Bayesian(BN) fault diagnosis model of traditional Chinese medicine dry granulation based on the failure model and effect analysis(FMEA), effectively control risk factors and ensure the quality of granules.Firstly, the risk ana-lysis of dry granulation process was carried out with FMEA, and the selected medium and high risk factors were taken as node variables to establish corresponding BN network with causality.According to the mathematical reasoning method of probability theory, the model was accurately inferred and verified by Netica, and the granule nonconformance was used as the evidence for reversed reasoning to determine the most likely cause of the failure that affected the granule quality.The BN fault diagnosis model of traditional Chinese medicine dry gra-nulation was established based on the medium and high risk factors of process, prescription and equipment screened out by FMEA, such as roller pressure, raw material viscosity, clearance between rollers in the paper.The fault diagnosis of traditional Chinese medicine dry granulation process was then carried out according to the model, and the posterior probability of each node under the premise of nonconforming granule quality was obtained.This method could provide strong support for operators to quickly eliminate faults and make decisions, so as to improve the efficiency and accuracy for fault diagnosis and prediction, with innovation in its application.
