R
Ricardo Dunia
Researcher at University of Texas at Austin
Publications - 45
Citations - 2098
Ricardo Dunia is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Principal component analysis & Fault detection and isolation. The author has an hindex of 15, co-authored 45 publications receiving 1987 citations. Previous affiliations of Ricardo Dunia include National Instruments & Cameron International.
Papers
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Identification of faulty sensors using principal component analysis
TL;DR: In this article, a sensor validity index (SVI) is proposed to determine the status of each sensor and the way the index is filtered represents an important tuning parameter for sensor fault identification.
Journal ArticleDOI
Subspace approach to multidimensional fault identification and reconstruction
Ricardo Dunia,S. Joe Qin +1 more
TL;DR: In this paper, the fundamental issues of detectability, reconstructability, and isolatability for multidimensional faults are studied using principal component analysis (PCA) and partial least squares.
Journal ArticleDOI
Joint diagnosis of process and sensor faults using principal component analysis
Ricardo Dunia,S. Joe Qin +1 more
TL;DR: In this article, a unified approach to process and sensor fault detection, identification, and reconstruction via principal component analysis is presented, which partitions the measurement space into a principal component subspace where normal variation occurs, and a residual subspace that faults may occupy.
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Determining the number of principal components for best reconstruction
S. Joe Qin,Ricardo Dunia +1 more
TL;DR: In this article, a well-defined variance of reconstruction error (VRE) is proposed to determine the number of principal components in a PCA model for best reconstruction, which avoids the arbitrariness of other methods with monotonic indices.
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Self-validating inferential sensors with application to air emission monitoring
TL;DR: A self-validating inferential sensor approach based on principal component analysis (PCA) is proposed, where the input sensors are validated using a fault identification and reconstruction approach proposed in Dunia et al.