[Iefac.list] IISE DAIS Webinar on Nov 29, 2018 - Analysis of Large Heterogeneous Repairable System: Reliability Data in Big Data Environment

[Du, Dongping]

The Data Analytics and Information Systems (DAIS) division at IISE would like to invite you to attend our fall webinar.

Analysis of Large Heterogeneous Repairable System: Reliability Data in Big Data Environment

Thursday, Nov. 29, 2 p.m. Eastern time | Register now<https://goto.webcasts.com/starthere.jsp?ei=1215055&tp_key=19caf9a1f1>
Presenter: Xiao Liu, Ph.D., assistant professor, University of Arkansas

In the age of Big Data, one pressing challenge facing engineers is to perform reliability analysis for a large fleet of heterogeneous repairable systems with covariates. In addition to static covariates, which include time-invariant system attributes such as nominal operating conditions, geo-locations, etc., the recent advances of sensing and IoT technologies have also made it possible to obtain dynamic sensor measurement of system operating and environmental conditions. As a common practice in the Big Data environment, the massive reliability data are typically stored on some distributed storage systems such as the Hadoop Distributed File System. Leveraging the power of modern statistical learning, this talk investigates a statistical approach which integrates the Random Forests algorithm and the classical data analysis methodologies for repairable system reliability, such as the nonparametric estimator for the Mean Cumulative Function and the parametric models based on the Nonhomogeneous Poisson Process. We show that the proposed approach effectively addresses some common challenges arising from practice, including system heterogeneity, covariate selection, model specification and data locality due to the distributed data storage. The large sample properties as well as the uniform consistency of the proposed estimator is established by extending existing theoretical results. The strengths of the proposed approach are demonstrated by comparison studies. A case study is presented to illustrate the application of the proposed approach on a real problem.

Xiao Liu, Ph.D., is an assistant professor at the Department of Industrial Engineering, University of Arkansas. Prior to that, he was a research scientist at IBM Thomas J. Watson Research Center, New York (2015 - 2017), and IBM Smarter Cities Research Collaboratory, Singapore (2012 - 2015). He also served as an adjunct assistant professor at the ISE Department of the National University of Singapore (2013 - 2016), and a post-doctoral on a joint project between Rutgers University and Qatar University (2011 - 2012). Dr. Liu's research focuses on industrial statistics, spatio-temporal modeling, and various engineering-knowledge-based data-driven methodologies in broad areas such as reliability testing, preventive maintenance, stochastic degradation, extreme weather events and urban air quality. Dr. Liu has published on IISE Transactions, Technometrics, Journal of Quality Technology, Annals of Applied Statistics, and among others. His work has also been recognized by awards including the Best Referred Paper Award from the QSR section at INFORMS 2016, IBM Outstanding Technical Achievement Award in 2015 and 2017, and the 2018 SPES award from American Statistical Association. Dr. Liu is currently on the editorial board of Quality and Reliability Engineering International (April 2016 - Present).

If interested, please register through the link https://goto.webcasts.com/starthere.jsp?ei=1215055&tp_key=19caf9a1f1.

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Dongping Du, Ph.D.
Assistant Professor
Department of Industrial, Manufacturing and Systems Engineering
Texas Tech University
PO Box 43061
Lubbock, TX  79409
Tel: 806-834-7388
E-mail: dongping.du at ttu.edu<mailto:dongping.du at ttu.edu>

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