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.
Subject(s)
Medicine, Chinese Traditional , Bayes Theorem , ProbabilityABSTRACT
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.
Subject(s)
Bayes Theorem , Medicine, Chinese Traditional , ProbabilityABSTRACT
Objective:To carry out the risk assessment on the factors in the process of granulation fluidized bed of traditional Chinese medicine(TCM) by using failure model and effect analysis(FMEA) and Bayesian network(BN), in order to effectively control risk factors and improve product quality. Method:The risk analysis of the fluidized bed granulation process was carried out by FMEA and the selected medium risk and high risk factors were taken as the main control points, the corresponding BN was established. The sensitivity analysis was used to screen out the main risk factors affecting particle fluidity, particle size uniformity, solubility and product cleanliness, the occurrence probability of each risk factor was determined by the evidence of unqualified particle quality, finally, taking fluidized bed granulation process of Sanye tablets as an example, the FMEA and BN were combined into the risk assessment process to verify the effectiveness and reliability of the method. Result:Based on the middle and high risk points of fluidized bed process, particle size of raw materials, moisture content and hygroscopicity of raw materials, dosage, concentration and addition amount of binder, cleaning degree and integrity of collection bag, and nozzle position, which were selected by FMEA, a fluidized bed granulation risk network with causality was constructed. Among them, hygroscopicity of raw materials, concentration and addition amount of binder, inlet temperature and atomization pressure were high probability risk factors, and the probability of occurrence were 55%, 63%, 59%and 58%, respectively. According to the Bayesian risk relationship network which controlled Sanye tablets fluidized bed granulation analysis results showed that the P values of inlet temperature, atomization pressure and concentration of binder were 0.003 4, 0.032 6 and 0.041 8, respectively in the regression model of influencing factors and particle size uniformity, indicating that there was a significant correlation between the three factors and the particle quality, which was basically consistent with the conclusion obtained by FMEA-BN method. Conclusion:The combination of FMEA and BN for visualized risk assessment of fluidized bed granulation helps to effectively control the risk factors in the granulation process, reduce product quality risks and provide strong support for the improvement of granulation process of TCM.
