Due to the high reliability of present meters, it is difficult to obtain the failure time of meters through accelerated life tests. Based on the failure data of the accelerated life test, this paper studies the mathematical model based on the Wiener process and establishes the degradation model of the instrument by the maximum likelihood to estimate the parameters of the Wiener model. With full consideration of the possible nonlinear effects in modeling, the time scale transformation method is used to study and obtain the reliability life prediction model of smart meters based on nonlinear data. Finally, the reliability life prediction model of meters is verified and evaluated through the example data of the accelerated life test of smart meters. Compared with the conventional method, this method has less error in calculating the reliability, greatly saves test time, and has a higher accuracy than estimating the lifetime model using the Wiener process directly.

Due to the high demand for compression recovery tests of metal gaskets, it is difficult to provide and evaluate uniform stress on metal gaskets. To effectively evaluate the uniformity of surface stress on a metal gasket, we propose a method of evaluating gasket surface stress based on gray-level co-occurrence matrix theory. Angular second moment and entropy are selected as the main characteristic parameters, and the evaluation indexes ($f_1$ and $f_2$) are proposed. The analysis is based on finite element calculation under different parameters of a gasket test rig. The angular second moment and entropy curve of the stress distribution cloud map are drawn according to the result of finite element calculation. The results show that the method can evaluate the uniformity of stress distribution. The best parameters of the gasket testing rig are obtained by analyzing the uniformity evaluation index. The method can realize rapid comparison and accurate evaluation of the gasket surface stress.