E
Evan L. Russell
Researcher at University of Illinois at Urbana–Champaign
Publications - 16
Citations - 3050
Evan L. Russell is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Dimensionality reduction & Principal component analysis. The author has an hindex of 10, co-authored 16 publications receiving 2948 citations. Previous affiliations of Evan L. Russell include ExxonMobil.
Papers
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Journal ArticleDOI
Fault Detection and Diagnosis in Industrial Systems
TL;DR: The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists.
Journal ArticleDOI
Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis
TL;DR: In this article, the authors developed an information criterion that automatically determines the order of the dimensionality reduction for FDA and DPLS, and show that FDA is more proficient than PCA for diagnosing faults, both theoretically and by applying these techniques to simulated data collected from the Tennessee Eastman chemical plant simulator.
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Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis
TL;DR: A residual-based CVA statistic proposed in this paper gave the best overall sensitivity and promptness, but the initially proposed threshold for the statistic lacked robustness, so increasing the threshold to achieve a specified missed detection rate was motivated.
Book
Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
TL;DR: This paper presents a meta-analysis of the Tennessee Eastman Process, an attempt to evaluate the methodology and techniques used in this type of analysis, as well as some of the approaches used in other approaches.
Journal ArticleDOI
Multidimensional realization of large scale uncertain systems for multivariable stability margin computation
TL;DR: In this paper, an algorithm is developed that reduces the dimension of the realizations while improving numerical accuracy, reducing computational expense, and reducing run-time memory requirements for large scale uncertain systems, which have large numbers of inputs, outputs, states and uncertain parameters.