Design of Fine Life Cycle Prediction System for Failure of Medical Equipment

Abstract

The inquiry process of traditional medical equipment maintenance management is complicated, which seriously affects the efficiency and accuracy of medical equipment maintenance management, and causes a lot of waste of manpower and materials. In order to accurately predict the failure of medical equipment, an accurate prediction system for failure life cycle of medical equipment was designed. The system is divided into four modules: the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage, the middle and the later stage; the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module; the main function of the fault diagnosis module is based on the medical equipment whole life cycle management module. The operation data of equipment is diagnosed by inference machine; the fault prediction module builds a fine prediction system based on least square support vector machine algorithm, and uses AFS ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters, and the optimal model is used to predict the medical equipment failure. In order to verify the effectiveness of the design system, comparative experiments are designed to verify. The results show that the designed system can accurately predict the failure of electrocardiogram diagnostic instrument and incubator, and has high support and reliability. Compared with the comparison system, the prediction error of the design system is the smallest and the program running time is the shortest. Therefore, the design system can accurately predict the different failure types and causes of medical devices.