Technical co-sponsors:

PHM Nondestructive Evaluation Panel

The panel will provide an overview of the development of rigorous approaches to designing the Nondestructive Evaluation (NDE) and Structural Health Monitoring (SHM) sensing systems to PHM.  The intelligent management of modern structures and systems operational life, damage assessment, and the state of structure is critical and becoming challenging due to the fast-pace progress in new materials and performance improvement.  NDE is the science of inspecting and evaluating structures, materials, and systems without detrimentally affecting their properties and usefulness.  SHM represents a shift away from routine nondestructive inspection, which involves “feedback-based prediction” of structural integrity.  Significant benefits can be achieved if current prognostics and health management (PHM) tools can be augmented and improved by NDE and SHM methodologies.  Each of the panelists will present information describing their experience and perspective based on specific areas of NDE and SHM.  The panel will entertain questions from the audience, providing an interactive forum to share knowledge and expand our shared understanding of future NDE and SHM system design and its major and critical impacts to the PHM community.

Moderator:

Yiming Deng, Assistant Professor, Electrical Engineering and Bioengineering, University of Colorado Denver, USA

Dr. Yiming Deng received his B. Sc. in electrical engineering from Tsinghua University, Beijing, China in 2003 and Ph.D. in electrical engineering from Michigan State University, East Lansing, MI, USA in 2009.  He is currently an Assistant Professor of Electrical Engineering at University of Colorado Denver (CU-Denver), USA since 2009.  He is also a scientific staff member at Colorado Translational Research Imaging Center (CTRIC).  His current research interests include:  structural health monitoring (SHM), hybrid electromagnetic imaging sensors, numerical modeling and simulation for nondestructive evaluation (NDE) and medical applications, medical imaging and image analysis, inverse problems.  Dr. Deng is a member of IEEE, ASNT, CCTSI and full member of Sigma Xi.  He is the winner of FAA-ATA Better Way Award in 2005 and ASNT Faculty Grant Award in 2010.

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 Panelists:

Jie (Peter) Liu, Assistant Professor, Department of Mechanical & Aerospace Engineering, Carleton University, Ottawa, Canada

Dr. Jie (Peter) Liu obtained his Ph.D. in Mechanical and Mechatronics Engineering from the University of Waterloo Canada in 2008.  Prior to his Ph.D. study, he worked for Invensys Controls for three years as a product engineer (1998-2000) and thereafter as a production manager (2000-2001).  After his Ph.D. study, he worked as a postdoctoral fellow at UC Berkeley for one and half years, working on the development of a regularized auxiliary particle filtering technique for nonlinear non-Gaussian system state estimation; during this period, he also conducted collaborative research with NASA Ames for remaining useful life prediction of lithium ion batteries.  He is currently an Assistant Professor in the Dept. of Mechanical & Aerospace Engineering at Carleton University, Ottawa, Canada.  He has been engaged in interdisciplinary research in the areas of machine condition monitoring and failure prognostics, intelligent mechatronic systems, vibration and system control for about eight years.

Title:  Bladed Disk Crack Detection

Abstract:  Health condition monitoring and fault diagnostics of turbo fan engines play significant roles in overall cost reduction and reliability enhancement of the aircraft system.  Among various types of potential faults in a turbo fan engine, crack initiation and propagation in the bladed disks of engines caused by high-cycle fatigue under cyclic loads are typical ones that could result in the breakdown of the engines if not detected at an early stage.  Reliable fault detection techniques are therefore critically needed to detect impending engine malfunctions as well as unexpected failures that could otherwise lead to costly and/or catastrophic consequences.  Although a number of approaches have been reported in literature, it still remains very challenging to develop a reliable technique to accurately estimate the health condition of bladed disks of engines.  In this talk, some recent research work, conducted by the presenter’s research group, for the development of an advanced signal processing technique for bladed disk crack detection is to be discussed.  The study is conducted on the spin rig test data that was acquired during a test on a TF39 fan using capacitance probes from Hood Technology.

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Tariq M. Khan, Assistant Professor, Pakistan Navy Engineering College, National University of Science and Technology, Pakistan

Dr. Tariq M. Khan received B.E. degree with honors in electrical engineering from National University of Science and Technology (NUST), Pakistan in 1999.  After bachelors, he performed engineer officer duties on board Pakistan Navy submarines for four years.  He completed his MS degree in electrical engineering from NUST in 2006 with a CGPA of 4.0/4.0.  Based on his outstanding performance, he was awarded NUST scholarship to pursue PhD studies in USA.  He received Ph.D. degree in electrical engineering from Michigan State University (MSU) in 2009.  He remained Institute of Electrical and Electronics Engineer (IEEE) student ambassador for year 2009.  He is also a recipient of Dissertation Completion Fellowship award from MSU Graduate School.  He did his two years post doctoral studies at Nondestructive Evaluation Lab, MSU where he remained involved in wide range of industry funded projects.  He has over 30 publications in International technical journals & proceedings.  In February 2012, he has joined NUST as Assistant Professor in department of Electronics and Power Engineering.  He is member of IEEE.  His research interests include the development and applications of sensors and statistical signal processing algorithms for a wide set of nondestructive evaluation and biomedical applications.  He is also interested in development of deterministic and statistical solutions to NDE inverse problems.  He is avid reader of books.  He enjoys water sports, listening music and playing with his daughters.

Title:  Particle Filter Based Data Fusion in the Prognosis Study for Predicting Remaining Useful Life of Steam Generator Tubing

Abstract:  Steam Generator (SG) tubes are used to transfer heat from the primary side to pressurized steam on the secondary side in a nuclear power plant.  Multi frequency Eddy current inspection of SG tubes is performed periodically and the data is analyzed to detect and characterize a variety of flaw categories in different locations of the SG tube geometry.  Different skin depth penetration capability of eddy current at different frequencies can be utilized for better flaw characterization.  This talk uses the data analysis results to estimate remaining useful life (RUL) of SG tubes in the nuclear reactor.  Knowledge of RUL will ensure corrective action before the occurrence of tube leakage which may lead to an accident.  A Monte Carlo based recursive algorithm known as particle filter is used to predict the RUL of the steam generator tubes.  The uncertainty involved in prediction of remaining useful life of tubes using particle filter algorithm is controlled by an RUL correction loop based on different prediction and modeling algorithms such as autoregressive algorithm.  This work is an extension to the previous work.  The technique is applied to actual multiple measurements mode data (multi-frequency eddy current inspection data of steam generator tubing) acquired at successive intervals to calculate RUL.  The predicted RUL is compared to the actual failure time of the tube to show the efficacy of the technique.  The results are also shown using single mode measurements.  Effect on the results using different prediction algorithms for controlling uncertainty in prediction of RUL is also discussed.

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Rui Liu, Postdoctoral Fellow, Civil Engineering, University of Colorado Denver, USA

Dr. Rui Liu is a postdoctoral researcher in the Department of Civil Engineering at the University of Colorado Denver.  His research interests lie in the field of sustainable urban infrastructure, especially in the areas of innovative structural engineering material, intelligent design, inspection, rating, maintenance, and repair of infrastructure.  He is the leading researcher to develop inspection guidelines for two cable pedestrian bridges (Highland Bridge and Platte River Pedestrian Bridge) on the 16th Street. of Denver for the City and County of Denver.  Besides research, he teaches undergraduate and graduate courses in mechanics of materials, matrix structural analysis, and finite element analysis.  He graduated with his Ph.D. in civil engineering from the University of Colorado.

Title:  Utilization of Vibrational Techniques in Bridge Health Monitoring

Abstract:  The average bridge in the U.S. is now 45 years old.  In 2008, approximately one in four rural bridges and one in three urban bridges were deficient.  American Association of State Highway and Transportation Officials estimated that it would cost roughly $140 billion to repair every deficient bridge in the country.  To remedy the current challenges, advanced technologies to augment current bridge inspection and evaluation need development.  There is continued interest in the utilization of vibrational techniques in the field of nondestructive evaluation and damage detection for the bridge health monitoring.  The direct relationship of stiffness, mass, and damping of a structure to the natural frequencies, mode shapes, and modal damping values, can be used for the purpose of structural health monitoring.  This talk includes a literature review that summarizes the basic approaches to the vibrational monitoring and concludes with a case study, in which the vibrational techniques are employed to evaluate the condition of the Platte River Pedestrian Bridge on the 16th Street of Denver.