Degradation Modeling
Inverse Gaussian processes, multivariate degradation, two-phase and two-scale reliability models.
Liangliang Zhuang
Seeking knowledge, leading with courage.
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
I attended the 2026 Annual Academic Conference of the Reliability Branch of the Operations Research Society of China at Beijing Forestry University and presented “Modeling Multivariate Degradation with Time-Varying Mean-Variance Dynamics for Reliability Assessment.”
Presented an online seminar at Reliability Home on “Reparameterized Inverse Gaussian Process and Its Applications.”
The paper “Modeling two-scale degradation with heterogeneity: A unified random-effects inverse Gaussian framework” was accepted by IISE Transactions.
参加在北京林业大学举办的中国运筹学会可靠性分会 2026 年学术年会，并报告“含时变均值-方差动态的多元退化建模与可靠性评估”。
在“可靠性之家”线上报告“重参数化逆高斯过程及其应用”。
论文“Modeling two-scale degradation with heterogeneity: A unified random-effects inverse Gaussian framework”被 IISE Transactions 接收。
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