Real-time residual life prediction based on kernel differential homeomorphic transformation

The real-time residual life prediction based on kernel density estimation does not make any assumptions about the data distribution form, but studies the distribution characteristics by the data itself, ?which avoids the problem that many existing data-driven prediction models need model structure assumption and parameter estimation but leads to inaccurate life prediction. However, when the traditional kernel density estimation is used to estimate the probability density of bounded variables, the estimation deviation will occur at the boundary of the interval, which will affect the accuracy of the remaining life prediction. For the above problems, a real-time residual life prediction method based on kernel diffeomorphism transformation estimation was proposed. The diffeomorphism transformation was used to transform the bounded random variable to the whole real number domain, and then transformed it to the kernel density estimation problem in the traditional sense. The feasibility and effectiveness of the proposed method was verified with real-time monitoring data, and the influence of the optimal initial sample size on the accuracy of real-time residual life prediction was analyzed. © 2020, Editorial Department of CIMS. All right reserved.