Degradation Modeling
Inverse Gaussian processes, multivariate degradation, two-phase and two-scale reliability models.
Liangliang Zhuang
Seek truth; dare to lead.
Reliability
Statistics
Bayesian
Degradation
Postdoc
PHM
RUL
Optimization
Research
Predictive Maintenance
Remaining useful life prediction, sensor degradation, Bayesian deep learning, and PHM workflows.
Bayesian & AI Systems
Hierarchical Bayesian inference, adaptive sampling, federated learning, and data-driven optimization.
Selected papers
Modeling two-scale degradation with heterogeneity: A unified random-effects inverse Gaussian framework
Liangliang Zhuang, Yizhong Ma, Guanqi Fang, and Ancha Xu*. IISE Transactions, 2026.
From statistical modeling to AI-integrated inverse Gaussian process: A comprehensive review for prognostics and health management
Liangliang Zhuang, Yizhong Ma, Jianjun Wang, Rong Pan, and Ancha Xu*. Engineering Management, 2026, 13(1): 65.
Multivariate reparameterized inverse Gaussian processes with common effects for degradation-based reliability prediction
Liangliang Zhuang, Ancha Xu*, Guanqi Fang, and Yincai Tang. Journal of Quality Technology, 57(1): 51-67.
Recent updates
Awarded a China Postdoctoral Science Foundation General Program grant.
Our data-driven KNN modeling paper was accepted by Applied Soft Computing.
Presented at the ZJUI Young Scholars Development Forum in Haining.
获中国博士后科学基金第 79 批面上资助。
数据驱动 KNN 建模论文被 Applied Soft Computing 接收。
在 ZJUI 青年学者发展论坛作学术报告。
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