Author
Evan L. Russell
Other affiliations: ExxonMobil
Bio: 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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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.
Abstract: 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. The material included is well organized with logical and clearly identified parts; the list of references is quite comprehensive and will be of interest to readers who wish to explore a particular subject in depth. The presentation of the subject material is clear and concise, and the contents are appropriate to postgraduate engineering students, researchers and industrialists alike. The end-of-chapter homework problems are a welcome feature as they provide opportunities for learners to reinforce what they learn by applying theory to problems, many of which are taken from realistic situations. However, it is felt that the book would be more useful, especially to practitioners of fault detection and diagnosis, if a short chapter on background statistical techniques were provided. Joe Au
1,553 citations
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.
586 citations
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.
456 citations
Book•
25 Feb 2000
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.
Abstract: I. Introduction.- 1. Introduction.- II. Background.- 2. Multivariate Statistics.- 3. Pattern Classification.- III. Methods.- 4. Principal Component Analysis.- 5. Fisher Discriminant Analysis.- 6. Partial Least Squares.- 7. Canonical Variate Analysis.- IV. Application.- 8. Tennessee Eastman Process.- 9. Application Description.- 10. Results and Discussion.- V. Other Approaches.- 11. Overview of Analytical and Knowledge-based Approaches.- References.
293 citations
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.
Abstract: SUMMARY The prevailing framework for robust stability and performance analysis requires that the uncertain system be written as a linear fractional transformation of the uncertain parameters. This problem is algebraically equivalent to the problem of deriving the state space realization for a multidimensional transfer function matrix, for which a systematic algorithm was recently provided by Cheng and DeMoor. 1 In this work an algorithm is developed that reduces the dimension of the realizations while improving numerical accuracy, reducing computational expense, and reducing run-time memory requirements. Such improvements are required for the realization of large scale uncertain systems, which have large numbers of inputs, outputs, states, and=or uncertain parameters.
26 citations
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TL;DR: A bibliographical review on reconfigurable fault-tolerant control systems (FTCS) is presented, with emphasis on the reconfiguring/restructurable controller design techniques.
2,455 citations
TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
2,263 citations
TL;DR: It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.
Abstract: This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction- and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contribution- and reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation. Copyright © 2003 John Wiley & Sons, Ltd.
1,408 citations
TL;DR: A basic data-driven design framework with necessary modifications under various industrial operating conditions is sketched, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.
Abstract: Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.
1,289 citations
TL;DR: A state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades are provided to draw attention from the systems and control community and the process control community.
1,154 citations