The Department of Systems Engineering and Engineering Management (SEEM) of City University of Hong Kong (CityU) organized a Seminar entitled “Physical Model-based Engineering Products and Systems Design with Assured Reliability and Resilience: Methodologies and Application”. This seminar aimed to introduced model-based reliability analysis, significant design errors and proactive maintenance such as Prognostics and Health Management (PHM). I was honor invited by Prof. Min XIE to attend Dr. Zhimin XI seminar and we took a photo for memory.
In the beginning, Prof. Min XIE gave welcome speech and introduced our guest speaker.
Dr. Zhimin XI (奚志敏) (Assistant Professor, Industrial and Systems Engineering, School of Engineering, Rutgers, The State University of New Jersey) introduced his talk content. Firstly, he explained why we need reliability and resilience because of many failures/errors happened such as in Aerospace System, Nuclear System, Automotive System and Structure System, etc.
Then Dr. XI introduced his research objectives that the first one is to build theoretical foundation to design highly reliable and resilient engineering systems.
The second objective is to create tangible industry impacts to design highly reliable and resilient engineering systems.
He said design error happened because of the model error. Then he introduced the Uncertainties in Model Validation (δ). For FE analysis, we needed to considered test uncertainty, model uncertainty and physical uncertainty. Then the framework for model-based prediction and design was discussed and he said effective control design errors resulting from model errors in the design of engineering products and systems.
And then Dr. XI used some study case to discuss how to determine and improve model accuracy in the intended uses of the model. Those challenges questions showed below:
- How to calibrate model parameters?
- How to characterize model bias?
- How to quantify model accuracy?
- How to improve model accuracy?The following diagram is one of examples to analysis model bias using response surface approach.
One of interested example was Lithium-ion Battery’s model uncertainty in Dr. XI’s study in 2014. He observed that battery State of Change (SOC) estimation using a typical extended Kalman Filter (KF) in fact is not very accurate where the error could range from 5% to 10% or even more depending on the battery characteristics.
After that Dr. XI overviewed of PHM including Health Sensing Function, Health Reasoning Function, Health Prognostics Function and Health Management Function. For Health Prognostics Function, it separated to Model-based and Data-Driven Approaches. He said we always lacked of data and proposed to use Copula-based sampling approach (Copula modeling). But inaccurate copula modeling could occur if insufficient training units.
Lastly, he explained aircraft engine prognostics to explain reliability and resilience for design and maintenance. Some case studies were briefed such as Battery Capacity Prognosis, Resilience-Based Design of a Microgrid System, Simulation of Layer-by-Layer Selective Laser Melting Process, and Uncertainties in Autonomous Vehicles.
After the seminar, we had dinner in CityU together.
(Left: Dr. Zhimin XI, Prof. Min XIE, Dr. Zijun ZHANG and I)