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
Our paper on multi-phase degradation modeling was accepted by Statistical Theory and Related Fields.
Presented at the Aviation Safety Big Data Workshop hosted by Shanghai University of International Business and Economics.
Presented “Reparameterized Inverse Gaussian Process and Its Applications” at the 2026 Tsinghua University Quality and Reliability Annual Conference.
多阶段退化建模综述论文被 Statistical Theory and Related Fields 接收。
参加上海对外经贸大学举办的航空安全大数据研讨会并作报告。
参加 2026 年清华大学质量与可靠性年会并作学术报告。
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