Abstract: Analytics of ‘Big Data’ has been so promising and useful that it is thought to be a new frontier for innovation, competition, and productivity in science and business. The ensemble of damage events measured when materials fail constitues an important set of “Big Data’. These events establish an information infrastructure that is the consequence of applied stress/strain. Such an ensemble of damage data is the manifestation of the source physics that can be used to quantify and classify types of material failure. Thus, nondestructive evaluation is in the frontline of ‘Big Data’ in the traditional core engineering fields. Presently, the analysis of information and knowledge embedded in NDE observed data is in its infancy and as such is not fully appreciated with regard to advancing our understanding the physics of damage, which has been overlooked by classical deterministic approaches. In this article, we established a framework of data-enabled science for analyzing NDE Big Data by multivariate statistics and data mining methodologies. To do so, we introduce a concept of ‘consequence-driven’ paradigm, the 3S principles of data-enabled NDE, and establish a multivariate data matrix of random damage. A series of statistical and numerical methods was introduced to enable analyses of NDE Big Data, and each was illustrated by examples of damage analysis taken from typical engineering materials. In addition, we also present several promising research topics in this emerging field of research.

  

About the Speaker: Professor Gang Qi received his Ph.D. in Mechanical Engineer from Texas Tech University, and is now a professor of Mechanical Engineering, The University of Memphis and holds an Endowed Chair professorship at Tianjin University of Science and Technology since 2011. He is a distinguished fellow of Acoustic Emission Working Group (AEWG, US), Chairman of AEWG, Vice Chairman of International Society on Acoustic Emission, has served on the editorial board of SAE Trans. (Society on Automotive Engineers), is a Founding Associate Editor, Journal of Data-Enabled Science and Engineering (in progress with Springer). He has published over 30 peer reviewed articles with h-index 10, over 60 conference presentations, and over 120 technical reports prepared for FAA.