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

Abstract

The inquiry process of traditional medical equipment maintenance management is complex, which has a negative impact on the efficiency and accuracy of medical equipment maintenance management and results in a significant amount of wasted time and resources. To properly predict the failure of medical equipment, a method for failure life cycle prediction of medical equipment was developed. 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 stage, 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; and the main function of the fault diagnosis module is based on the normal value of the relevant sensitive data in the whole life cycle management module. The inference machine diagnoses the operation data of the equipment; the fault prediction module constructs a fine prediction system based on the least square support vector machine algorithm and uses the AFS-ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters; the optimal model is then used to predict the failure of medical equipment. Comparative experiments are designed to determine whether or not the design system is effective. The results demonstrate that the suggested system accurately predicts the breakdown of ECG diagnostic equipment and incubators and has a high level of support and dependability. The design system has the minimum prediction error and the quickest program execution time compared to the comparison system. Hence, the design system is able to accurately predict the numerous causes and types of medical device failure.