2020_08_A Reliability Prediction Model with Machine Learning Capabilities

No Image Description
The Presentation:
Rapid developments in information technologies enabled recording big data environments in near real-time. Such big data environments provide an unprecedented opportunity for efficient event detection and therefore effective reliability models, but they also pose interesting challenges. One challenge is modeling the number of recurrent events for heterogeneous subpopulations with limited records. To address this challenge, a power–law nonhomogeneous Poisson process (NHPP) with machine learning capabilities is proposed. The scale parameter of the Poisson process is learned for each individual subpopulation. However, the shape parameter is learned for latent groups that each consists of multiple (internally homogenous) subpopulations. The proposed Poisson process collaboratively models multiple heterogeneous subpopulations; therefore, it allows transferring knowledge between subpopulations and diminishes the chances of overfitting. Simulation and real-life case studies showed the high modeling accuracy of the proposed approach.
The Presenters: Abdallah Chehade (UM) and Vasiliy Krivtsov (Ford)
Abdallah Chehade is an Assistant Professor in the Department of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn. Dr. Chehade received the B.S. degree in mechanical engineering from the American University of Beirut, Beirut, Lebanon, in 2011 and the M.S. degree in mechanical engineering, the M.S. degree in industrial engineering, and the Ph.D. in industrial engineering from the University of Wisconsin-Madison in 2014, 2014, and 2017, respectively. His research interests are explainable AI, synthetic data generation, optimized data analytics, and data fusion for statistical process modeling. Dr. Chehade is a member of INFORMS, IEEE, and IISE.

Vasiliy Krivtsov is the Director of Reliability Analytics at the Ford Motor Company. He also holds the position of Adjunct Professor at the University of Maryland, where he teaches a graduate course on advanced reliability data analysis.
 Krivtsov has earned a PhD degree in Electrical Engineering from Kharkov Polytechnic Institute (Ukraine) and a PhD in Reliability Engineering from the University of Maryland (USA). He is the author of over 60 professional publications, including 3 books on Reliability Engineering & Risk Analysis and 16 inventions, including 6 corporate inventions on statistical algorithms for Ford. He is a Vice Chair of the International Reliability Symposium (RAMS®) Tutorials Committee and a Senior Member of IEEE. Prior to Ford, Krivtsov held the position of Associate Professor of Electrical Engineering in Ukraine, and that of Graduate Research Scientist at the University of Maryland Center for Reliability Engineering. Further information on Dr. Krivtsov's professional activity is available at www.krivtsov.net
Media Type
File, Video, Image
2020_08_WebinarRecording.zip2020_08_WebinarRecording.zipRecording for Webinar209419 KB
_2020_0819_GuangbinFinal.pdf_2020_0819_GuangbinFinal.pdf2020_0819_Guangbin Flyer for A Reliability Prediction Model with Machine Learning Capabilities 203 KB
2020+GY+Symposium+ML+NHPP+-+final.pdf2020+GY+Symposium+ML+NHPP+-+final.pdfPresentation File for A Reliability Prediction Model with Machine Learning Capabilities 1902 KB

Resource Details

Average Rating:
Date Added: Aug 29, 2020
Category: Resources
Edit Item Photos