Abstract

Info

Author(s):: FAN Litian; JIANG Jinda; XIA Jingtao; CUI Feiyi; MIAO Jichang; WANG Tingting; XIA Honglin; WANG Shengjun; CHEN Hongwen; Department of Medical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

Keywords:: Keywords: artificial neural network statistical distribution reliability prediction fitting accuracy

Abstract:: Abstract: A system reliability prediction method based on artificial neural network is proposed. The network is trained with the system failure data and the corresponding estimate of median rank estimate. A large number of failure time points are selected in the system failure duration, and their predicted cumulative distribution function values are found. Subsequently, the cumulative distribution function curve, probability density function curve, and failure rate function curve of the system are derived using spline regression method. To verify the superiority of the artificial neural network model, taking the failure data of 5 systems such as infant incubator as an example, the proposed model is compared with the statistical distribution models such as Weibull, Fréchet and Logistic using the coefficient of determination R2, mean square error and log-likelihood function. The results show that the artificial neural network fits the best.