R
Raghunathan Rengaswamy
Researcher at Indian Institute of Technology Madras
Publications - 225
Citations - 10538
Raghunathan Rengaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Proton exchange membrane fuel cell & Fault detection and isolation. The author has an hindex of 39, co-authored 210 publications receiving 9632 citations. Previous affiliations of Raghunathan Rengaswamy include Indian Institute of Technology Bombay & Bosch.
Papers
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A Systematic Framework for the Development and Analysis of Signed Digraphs for Chemical Processes. 1. Algorithms and Analysis
TL;DR: In this paper, the authors focus on the systematic development of graph models and the conceptual relationship between the analysis of graph model and the underlying mathematical description and the analysis procedures for the graph model.
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Control Loop Performance Assessment. 2. Hammerstein Model Approach for Stiction Diagnosis
TL;DR: In this work, a new identification procedure for Hammerstein systems that supports stiction diagnosis is proposed and an optimization approach is used to jointly identify the process model and the stiction parameter.
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A qualitative shape analysis formalism for monitoring control loop performance
TL;DR: This paper proposes an automated qualitative shape analysis formalism for detecting and diagnosing different kinds of oscillations in control loops and extends the earlier QSA methodology to make it more robust by developing an algorithm for automatic identification of the appropriate global time-scales.
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Recursive estimation in constrained nonlinear dynamical systems
TL;DR: In this article, recursive nonlinear dynamic data reconciliation (RNDDR) and a combined predictor-corrector optimization (CPCO) method were proposed for efficient state and parameter estimation in nonlinear systems.
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A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis
TL;DR: A combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis that combines the completeness property of SDG with the high diagnostic resolution property of QTA.