Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
By E. L. Russell, Leo H. Chiang, Richard D. Braatz. 2000
337 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/95f358fea177bb50d80da473c7503cd8d86be7f8
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In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Bibliographical review on reconfigurable fault-tolerant control systems (2003, neutral): “…2000; Isermann, 2005; Venkatasubramanian, Rengaswamy, Yin, et al., 2003; Venkatasubramanian, Rengaswamy, & Kavuri, 2003; Venkatasubramanian, Rengaswamy, Kavuri, et al., 2003) and books (Chiang et al., 2001; Isermann, 2006; Russell et al., 2000; Vachtsevanos et al., 2006; Witczak, 2007) for detail.”
- A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, positive): “Because of their simple forms and low design efforts compared with the model-based techniques, the multivariate statistical methods are widely used for process monitoring in numerous industrial applications [35]–[40].”
- A review of process fault detection and diagnosis: Part I: Quantitative model-based methods (2003, neutral): “Recent books on fault detection and diagnosis have been written by Gertler (1998) and Chen and Patton (1999) and Russell, Chiang, and Braatz (2000).”