Tutorials

Tutorials

One of the unique features of the PHM conferences is the free technical tutorials on various topics with comprehensive introduction to the state-of-the-art. The proposed tutorials address the interests of varied audience: beginners, developers, designers, researchers, practitioners, and decision makers who wish to learn a given aspect of PHM.

Battery Capacity Forecasting and Early Life Prediction

Prof. Chao Hu
Iowa State University
United States

Biography Abstract

Biography
Dr. Chao Hu received his B.E. degree in Engineering Physics from Tsinghua University in Beijing, China in 2007 and the Ph.D. degree in Mechanical Engineering at the University of Maryland, College Park in Maryland in 2011. He worked first as a Senior Reliability Engineer and then as a Principal Scientist at Medtronic in Minnesota from 2011 to 2015. He is currently an Assistant Professor in the Department of Mechanical Engineering at Iowa State University and has a courtesy faculty appointment in the Department of Electrical and Computer Engineering. His research interests are engineering design under uncertainty, lifetime prediction of lithium-ion batteries, and prognostics and health management. Dr. Hu has received several awards and recognitions for his research, including: the Best Track Paper Award at the IISE Annual Conference & Expo in 2019; the ASME Design Automation Young Investigator Award in 2018; the Highly Cited Research Paper 2012-2013 in the Journal of Applied Energy in 2015; the Star of Excellence Individual Award at Medtronic in 2014; and the Best Paper Awards at the ASME Design Automation Conference and the IEEE International Conference on Prognostics and Health Management in 2013 and 2012, respectively.
Abstract
Over the past decade, a large and diverse set of battery prognostics techniques have been developed for forecasting the capacity and predicting the remaining useful life of lithium-ion batteries. The past two years has seen increasing research efforts developing and validating machine learning algorithms and models for early life prediction. The idea is to predict battery cycle life using early-cycle data where battery cells show negligible capacity degradation. This tutorial will review past and ongoing research studies on battery capacity forecasting and early life prediction and discuss the long-term testing and methodology development efforts led by a team of researchers at Iowa State University.
TBA

Dr. Yvonne Lu
University of Oxford
England

Biography Abstract

TBA

Dr. Samaneh Kouchaki
University of Surrey
England

Biography Abstract

Mobility meets PHM

Prof. Ki-Yong Oh
Chung-Ang University
Republic of Korea

Biography Abstract

Biography
Ki-Yong Oh received his B.S. degree in Mechanical Engineering from Hanyang University, Seoul, Korea, in 2005, M.S. degree in Mechanical Engineering from KAIST in 2006, and Ph.D. in Mechanical Engineering from University of Michigan, Ann Arbor, in 2016. He worked as a senior researcher at Korea Electric Power Corporation Research Institute from 2008 to 2017. He joined the School of Energy System Engineering at the Chung-Ang University in 2017, where he is currently employed as an assistant professor. Dr. Oh’s research interests include applied dynamics, and mobility/AI toward prognostics and health management in the field of complex energy systems. Dr. Oh has received several awards and recognitions for his research, including: Research excellent award at Chung-Ang University in 2021; Best paper award at ICVISP conference in 2019; Young Scientist Award at PHM Asia Pacific in 2017, and Gold Meal for Outstanding Performance at iNEA in 2011.
Abstract
Mobility is a core technology with internet of things and artificial intelligence for the 4th industrial revolution. The mobility is also effective in the research field of prognostics and health management (PHM) in the sense that this technology not only provides an easy way to approach any facility of interest, but also replaces human’s duty in a dangerous environment for condition monitoring and fault detections. This tutorial presents the mobility toward PHM with smart inspection systems for overhung and underground power transmission lines. Inspection of power transmission lines at severe environments would be a good example to demonstrate effectiveness of mobility because power transmission lines are wide-spread, located at a variety of regions, and suffer from severe environments. A field demonstration of these platforms confirm inherent advantages of the mobility.
Model driven digital twin of vibro-acoustic system

Prof. Jin-Gyun Kim
Kyung Hee University
Republic of Korea

Biography Abstract

Biography
Jin-Gyun Kim received his B.S. degree in Civil Engineering from Korea University in 2008, M.S. degree in Civil Engineering from Korea University in 2010, and Ph.D. degree in ocean systems engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2014. He worked as a senior researcher in Korea Institute of Machinery and Materials (KIMM) from 2014 to 2017. He joined Department of Mechanical Engineering at Kyung Hee University in 2018, where he is currently employed as an assistant professor. Dr. Kim’s research interests include computational dynamics, vibration, model based digital twin/virtual sensing, multiphysics modeling and simulation, etc. Dr. Kim has received several awards and recognitions for his research, including: Young Scientist Award at KSME dynamic and control division in 2020, Best annual scientific award at KIMM in 2016, and Doctoral dissertation award at KAIST in 2014.
Abstract
Digital twin is a core technology to prognostic and health management (PHM). Model driven digital twin may provide more information of physical assets than data driven digital twin, but it requires much more computational burden. To handle this issue, many CAE software with twin builder (e.g. ANSYS, Siemens, etc) include various reduced-order modeling (ROM) techniques for linear and nonlinear systems. The tutorial presents an effective model driven digital twin framework of vibro-acoustic interaction problem with multiphysics model reduction. A real-time virtual sensor of fluid-filled pipe implemented by the framework will be also introduced. The virtual sensor can estimate the unmeasured time transient responses of a vibro-acoustic structure in real time. The tutorial includes a theoretically review of vibro-acoustic model reduction with its example codes.