Di Liu
Orcid: 0000-0002-8232-4089Affiliations:
- Beihang University, School of Automation Science and Electrical Engineering, Beijing, China
According to our database1,
Di Liu
authored at least 21 papers
between 2018 and 2025.
Collaborative distances:
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Bibliography
2025
A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function.
Qual. Reliab. Eng. Int., February, 2025
Physics-informed neural network supported wiener process for degradation modeling and reliability prediction.
Reliab. Eng. Syst. Saf., 2025
A Multiobjective Optimization Method for Collecting and Releasing Processes of Winch System Considering Wave Disturbance and Control Laws.
Int. J. Intell. Syst., 2025
Fault diagnosis using liquid state machine with spiking-timing-dependent plasticity learning rule.
Expert Syst. Appl., 2025
A nonparametric degradation modeling method based on generalized stochastic process with B-spline function and Kolmogorov hypothesis test considering distribution uncertainty.
Comput. Ind. Eng., 2025
A degradation modeling method based on artificial neural network supported Tweedie exponential dispersion process.
Adv. Eng. Informatics, 2025
2024
Research on Classification Maintenance Strategy for More Electric Aircraft Actuation Systems Based on Importance Measure.
Symmetry, September, 2024
Reliab. Eng. Syst. Saf., 2024
GA based construction of maximin latin hypercube designs for uncertainty design of experiment with dynamic strategy management.
Appl. Soft Comput., 2024
2023
A reliability estimation method based on signal feature extraction and artificial neural network supported Wiener process with random effects.
Appl. Soft Comput., March, 2023
A reliability estimation method based on two-phase Wiener process with evidential variable using two types of testing data.
Qual. Reliab. Eng. Int., February, 2023
A Method for Degradation Modeling and Prediction Based on Inverse Gaussian Process Supported by Artificial Neural Network.
Proceedings of the 9th International Symposium on System Security, Safety, and Reliability, 2023
2022
An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error.
Reliab. Eng. Syst. Saf., 2022
Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process.
Appl. Math. Comput., 2022
2021
A Glucose-Insulin Mixture Model and Application to Short-Term Hypoglycemia Prediction in the Night Time.
IEEE Trans. Biomed. Eng., 2021
An artificial neural network supported stochastic process for degradation modeling and prediction.
Reliab. Eng. Syst. Saf., 2021
Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process.
Reliab. Eng. Syst. Saf., 2021
Reliability estimation by fusing multiple-source information based on evidential variable and Wiener process.
Comput. Ind. Eng., 2021
2020
A degradation modeling and reliability estimation method based on Wiener process and evidential variable.
Reliab. Eng. Syst. Saf., 2020
An evidence theory based model fusion method for degradation modeling and statistical analysis.
Inf. Sci., 2020
2018
Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process.
Reliab. Eng. Syst. Saf., 2018