Gao Di, Wang Ya-Jing, Wang Yan-Wen, Ye Xiang-Yin, Wang Yu, Wang Xiao-Yu, Huang Zan-Yang
Zhongguo Zhong Yao Za Zhi. 2020 Dec;45(24):5982-5987. doi: 10.19540/j.cnki.cjcmm.20200911.311.
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.
本文旨在基于失效模式与效应分析（FMEA）构建中药干法制粒的贝叶斯网络（BN）故障诊断模型，有效控制风险因素，确保颗粒质量。首先，运用FMEA对干法制粒过程进行风险分析，选取筛选出的中高风险因素作为节点变量，建立具有因果关系的相应BN网络。依据概率论的数学推理方法，通过Netica对模型进行精确推理与验证，并以颗粒不合格作为反向推理的证据，确定影响颗粒质量的最可能失效原因。基于FMEA筛选出的工艺、处方及设备等方面的中高风险因素，如本文中的辊压压力、原料黏度、辊筒间隙等，建立了中药干法制粒的BN故障诊断模型。然后根据该模型对中药干法制粒过程进行故障诊断，得出颗粒质量不合格前提下各节点的后验概率。该方法可为操作人员快速排除故障和决策提供有力支持，从而提高故障诊断与预测的效率和准确性，具有应用创新。
