高迪;王亚静;王雁雯;叶相印;王宇;王晓宇;黄赞扬;
GAO Di;WANG Ya-jing;WANG Yan-wen;YE Xiang-yin;WANG Yu;WANG Xiao-yu;HUANG Zan-yang;Tianjin University of Traditional Chinese Medicine;Engineering Research Center of Modern Chinese Medicine Discovery and Preparation Technique, Ministry of Education, Tianjin University of Traditional Chinese Medicine;

• 1:天津中医药大学
• 2:天津中医药大学现代中药发现与制剂技术教育部工程研究中心

摘要(Abstract)：

基于失效模式与效应分析(failure model and effect analysis, FMEA)构建中药干法制粒贝叶斯(Bayesian network,BN)故障诊断模型,有效控制风险因素,保证颗粒质量。首先运用FMEA辨识中药干法制粒过程的中、高风险作为节点变量建立具有因果关系的BN模型。依据概率论的数学推理方法对该模型进行精确推理及Netica验证,并以颗粒不合格为证据进行反向推理,逐级确定影响颗粒质量最可能的故障原因。该文基于FMEA筛选出的滚轮压力、原料黏性、滚轮间间隙等工艺、处方、设备方面的中、高风险因素构建中药干法制粒的BN故障诊断模型,并根据该模型对中药干法制粒工艺进行故障诊断,得出颗粒质量不合格的前提下各节点的后验概率。该方法可为操作人员迅速排故及做出决策提供有力支持,以提高故障诊断及预测的效率及精度,有较好的应用创新性。
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.

关键词(KeyWords)： 失效模式与效应分析;贝叶斯网络;推理验证;故障诊断;中药;干法制粒;颗粒质量
failure mode and effect analysis;Bayesian network;reasoning verification;fault diagnosis;traditional Chinese medicine;dry granulation;particle quality

Abstract:

Keywords:

基金项目(Foundation): 国家“重大新药创制”科技重大专项(2018ZX09721-005);; 天津市科技计划项目(18ZXXYSY00130,19ZYPTJC00060)

作者(Author): 高迪;王亚静;王雁雯;叶相印;王宇;王晓宇;黄赞扬;
GAO Di;WANG Ya-jing;WANG Yan-wen;YE Xiang-yin;WANG Yu;WANG Xiao-yu;HUANG Zan-yang;Tianjin University of Traditional Chinese Medicine;Engineering Research Center of Modern Chinese Medicine Discovery and Preparation Technique, Ministry of Education, Tianjin University of Traditional Chinese Medicine;

Email:

DOI: 10.19540/j.cnki.cjcmm.20200911.311

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