Haitao Liao Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Professor, John & Mary Lib White Systems Integration Chair

University of Arkansas at Fayetteville

faculty

39 h-index 245 pubs 6,059 cited

Research Areas

Reliability Engineering and Maintenance Risk and Resilience Analysis Machine Learning Applications Critical Infrastructure Resilience System Degradation Modeling Advanced Neural Network Applications Optimization Techniques

Links

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OverviewAI-generated summary

Haitao Liao's research focuses on reliability, risk, and resilience engineering, particularly for complex systems. His work investigates methods for predicting the remaining useful life of components and determining optimal maintenance schedules, often employing statistical models like the Gamma process and nonlinear Wiener process. Liao has also explored physics-informed machine learning for degradation modeling in electro-hydrostatic actuator systems and developed condition-based maintenance strategies for multi-state systems operating under varying environmental conditions.

His research extends to the optimal restoration of critical infrastructures following disruptions, considering both risk and resilience factors under uncertainty. Liao has also contributed to the development of optimal condition-based opportunistic maintenance and spare parts provisioning for multi-unit systems using state space partitioning. His recent work includes contributions to aerial image segmentation using multi-resolution transformers. Liao leads a research group and has published extensively, with a h-index of 39 and over 6,000 citations. He collaborates with several researchers at the University of Arkansas at Fayetteville, including Chase Rainwater, Roy McCann, Kelly M. Sullivan, and Heather Nachtmann.

Metrics

h-index: 39
Publications: 245
Citations: 6,059

Selected Publications

A predictive maintenance framework based on real-time credibility evaluation of remaining useful life prediction results (2025) DOI
S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling (2025) DOI
A Quantitative Maintenance Policy Development Framework for a Fleet of Self‐Service Systems (2025) DOI
Generalized Functional Mixed Models for Accelerated Degradation-Based Reliability Analysis (2024) DOI
Fleet Service Reliability Analysis of Self-Service Systems Subject to Failure-Induced Demand Switching and a Two-Dimensional Inspection and Maintenance Policy (2024) DOI
AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation (2024) DOI
A chance-constrained net revenue model for online dynamic predictive maintenance decision-making (2024) DOI
Optimal resilience-based restoration of a system subject to recurrent dependent hazards (2024) DOI
A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosol Optical Depth (2024) DOI
Optimal maintenance over a finite time horizon for a system under imperfect inspection and dynamic working environment (2024) DOI
Real-Time Evaluation of the Credibility of Remaining Useful Life Prediction Result (2024) DOI
System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System (2023) DOI
From Reliability to Resilience: More Than Just Taking One Step Further (2023) DOI
Accelerated Testing and Smart Maintenance: History and Future (2023) DOI
Structural Vulnerability Analysis of Interdependent Electric Power and Natural Gas Systems (2023) DOI