Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process. - Di Liu 012; Shaoping Wang; Chao Zhang 027; Mileta M. Tomovic (Reliab. Eng. Syst. Saf., 2018)

An evidence theory based model fusion method for degradation modeling and statistical analysis. - Di Liu 012; Shaoping Wang; Mileta M. Tomovic; Chao Zhang 027 (Inf. Sci., 2020)

A degradation modeling and reliability estimation method based on Wiener process and evidential variable. - Di Liu 012; Shaoping Wang (Reliab. Eng. Syst. Saf., 2020)

Reliability estimation by fusing multiple-source information based on evidential variable and Wiener process. - Di Liu 012; Shaoping Wang; Chao Zhang 027 (Comput. Ind. Eng., 2021)

Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process. - Di Liu 012; Shaoping Wang (Reliab. Eng. Syst. Saf., 2021)

An artificial neural network supported stochastic process for degradation modeling and prediction. - Di Liu 012; Shaoping Wang (Reliab. Eng. Syst. Saf., 2021)

A Glucose-Insulin Mixture Model and Application to Short-Term Hypoglycemia Prediction in the Night Time. - Weijie Wang; Shaoping Wang; Xingjian Wang; Di Liu 012; Yixuan Geng; Teresa Wu (IEEE Trans. Biomed. Eng., 2021)

Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process. - Di Liu 012; Shaoping Wang; Chao Zhang 027 (Appl. Math. Comput., 2022)

An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error. - Di Liu 012; Shaoping Wang; Xiaoyu Cui (Reliab. Eng. Syst. Saf., 2022)

A Method for Degradation Modeling and Prediction Based on Inverse Gaussian Process Supported by Artificial Neural Network. - Xiaochuan Duan; Di Liu 012; Shaoping Wang; Yaoxing Shang (ISSSR, 2023)

A reliability estimation method based on two-phase Wiener process with evidential variable using two types of testing data. - Jian Shi; Yajing Qiao; Shaoping Wang; Xiaoyu Cui; Di Liu 012 (Qual. Reliab. Eng. Int., 2023)

A reliability estimation method based on signal feature extraction and artificial neural network supported Wiener process with random effects. - Di Liu 012; Xiaochuan Duan; Shaoping Wang; Xiaoyu Cui; Xiao Wu; Yu Niu; Jian Shi (Appl. Soft Comput., 2023)

GA based construction of maximin latin hypercube designs for uncertainty design of experiment with dynamic strategy management. - Dong Liu; Shaoping Wang; Jian Shi; Di Liu 012 (Appl. Soft Comput., 2024)

Reliability model based on fault energy dissipation for mechatronic system. - Yajing Qiao; Shaoping Wang; Jian Shi; Di Liu 012; Mo Tao (Reliab. Eng. Syst. Saf., 2024)

Research on Classification Maintenance Strategy for More Electric Aircraft Actuation Systems Based on Importance Measure. - Xiaoyu Cui; Xuanhao Li; Zhiyao Zhao; Jiabin Yu; Di Liu 012 (Symmetry, 2024)

A degradation modeling method based on artificial neural network supported Tweedie exponential dispersion process. - Zhongze He; Shaoping Wang; Di Liu 012 (Adv. Eng. Informatics, 2025)

A nonparametric degradation modeling method based on generalized stochastic process with B-spline function and Kolmogorov hypothesis test considering distribution uncertainty. - Zhongze He; Shaoping Wang; Di Liu 012 (Comput. Ind. Eng., 2025)

Fault diagnosis using liquid state machine with spiking-timing-dependent plasticity learning rule. - Yi Wan; Shaoping Wang; Di Liu 012 (Expert Syst. Appl., 2025)

Physics-informed neural network supported wiener process for degradation modeling and reliability prediction. - Zhongze He; Shaoping Wang; Jian Shi; Di Liu 012; Xiaochuan Duan; Yaoxing Shang (Reliab. Eng. Syst. Saf., 2025)

A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function. - Di Liu 012; Xiaochuan Duan; Shaoping Wang; Jian Shi; Yaoxing Shang (Qual. Reliab. Eng. Int., 2025)