The primary goal of the system modeling and health monitoring team is to develop or adopt new analytical techniques to assess the impact of harsh environments on the structural integrity and health condition of offshore operating systems. The method and new models produced will then be given to Cross-Functional Team 1 to help produce a holistic view of risk.
Team Leads
Systems modeling and health monitoring cross-functional team leads are:
- Bilal Ayyub (University of Maryland)
- James Kaihatu (Texas A&M University)
- Ziaul Hugue (Prairie View A&M University)
Focus Areas
The team’s focus areas are:
- System hierarchies and models: Physical-human-cybersystem and system-of-systems models based on a set context offer a method for risk analysis and management; and will provide a basis for defining a new framework for risk-based reliability management.
- Human factors and reliability: We will consider human and organizational factors, errors and reliability models to facilitate risk analysis toward economic valuation and risk management.
- Probabilistic physics of failure models: We will develop models for varied failure modes and probabilistic methods to spatially and temporally characterize degradation and reliability for assessing failure profiles and examining appropriate recovery profiles.
- Loss functions and economic valuation: We will characterize and quantify risks that will facilitate trade-off, cost-benefit analysis, mitigation, countermeasure analysis, and associated economics by assessing, estimating, propagating and combining losses associated with potential failures.
- Strategies for resilience and sustainability: The team will examine system models and provide the basis for risk control, resilience and sustainability in quantifiable terms to perform associated economics and inform decision-making practices.
- Prognostic health monitoring: Real-time sensing of critical functional and physical parameters to address the probability of failure mechanisms such as corrosion will allow for monitoring of ocean infrastructure. Deep learning techniques (Cross-Functional Team 2) will be used to process and analyze data collected from the sensory network and other sources; and also to update risk factors.
