Research And Application Of Fault Prediction Technology Of Hemodialysis Conductance Measurement Module Based On Data Mining

As the main equipment of hemodialysis treatment,the hemodialysis machine is closely related to the patient's health.The conductivity measurement module is one of the most important monitoring modules.Once a failure occurs,it will cause great harm to the patient's health.Therefore,it is very important to find an effective method to predict the failure of the hemodialysis electroconductivity measurement module.At present,the equipment failure prediction methods used by hemodialysis enterprises are relatively traditional and single,and there are problems such as low accuracy and poor efficiency.In the context of the new intelligent manufacturing application project of the Ministry of Industry and Information Technology in 2018-"High-performance medical equipment(dialysis machine and dialyzer)intelligent manufacturing workshop construction" project background,in light of the above problems,research and design a Support vector machine fault prediction method for artificial fish school optimization.First,the defects of the support vector machine are improved;then,the improved fault prediction method is simulated and verified by combining with actual data;finally,the software of fault prediction system based on the hemodialysis electromechanical measurement module is designed and built.The work of this article mainly includes the following aspects:1.Study the internal structure and working principle of the hemodialysis machine,study the key parameters of each part of the hemodialysis machine,as well as the common fault conditions and causes,and then combine the actual production line investigation to determine the hemodialysis electromechanical measurement module as the fault prediction research point.2.The support vector machine and artificial fish school algorithm involved in this paper are studied.In view of the shortcomings such as large calculation amount and low efficiency of support vector machine,this paper uses artificial fish school algorithm to improve the support vector machine,and then gives the artificial fish school optimization Support vector machine algorithm to build a fault prediction model for hemodialysis electro-conductivity measurement module.The model is applied to the monitoring data set of the conductivity measurement module of a blood purification company.By comparison,the improved algorithm is superior to the original support vector machine algorithm in terms of prediction accuracy.It also verifies that the fault prediction method designed in this paper has the expected Failure prediction effect.3.Analyzed the need for the fault prediction of the conductivity measurement module,designed the fault prediction system for the hemodialysis conductivity measurement module,first designed the overall architecture of the system,then designed the functional module according to the functional requirements of the system,and finally used Java in conjunction with the software functional requirements Programming realizes the software of fault prediction system,and then realizes some functions of the software.According to the experimental simulation results,the fault prediction method of the hemodialysis electroconductivity measurement module based on data mining in this paper can achieve the expected fault prediction effect.