FDI based on causal graphical approaches for nonlinear processes

Smaili, Rahma; Harabi, Rafika El; Abdelkrim, Mohamed Naceur

doi:10.1109/ssd.2013.6564112

Cited by 2 publications

(2 citation statements)

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“…After all in our previous works [25,26], case studies show that SDG-based initial response analysis helps in fault diagnosis, for nonlinear systems, considerably.…”

Section: Introductionmentioning

confidence: 92%

Fault Diagnosis Algorithms by Combining Structural Graphs and PCA Approaches for Chemical Processes

Harabi

Smaili

Abdelkrim

2014

Chaos Modeling and Control Systems Design

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This work presents a diagnosis algorithm that combines structural causal graphical model and nonlinear dynamic Principal Component Analysis (PCA) for nonlinear systems with coupled energies incorporate the chemical kinetics of an equilibrated reaction, heat and mass transport phenomena. Therein, a coupled Bond Graph (BG) model, as an integrated decision tool, is used for modeling purpose. A Signed Directed Graph (SDG) is then deduced. A fault detection step is later carried out by generating initial responses through causal paths between exogenous and measured variables. After that, the localization of the actual fault is performed based on a nonlinear PCA (NLPCA) and back/forward propagations on the SDG. Simulation results on a pilot reactor show that the physic-chemical defects such as matter leakage, thermal insulation, or appearance of secondary reaction or temperature runaway when a very exothermic reaction occurs, can be detected and isolated.

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“…After all in our previous works [25,26], case studies show that SDG-based initial response analysis helps in fault diagnosis, for nonlinear systems, considerably.…”

Section: Introductionmentioning

confidence: 92%

Fault Diagnosis Algorithms by Combining Structural Graphs and PCA Approaches for Chemical Processes

Harabi

Smaili

Abdelkrim

2014

Chaos Modeling and Control Systems Design

Self Cite

View full text Add to dashboard Cite

show abstract

“…To overcome this parametrization problem, an auto-associative neural network Kramer (1991) has been used. After all in our previous works Smaili et al (2012b), Smaili et al (2012a) diagnosis based on BG is done through generation ARR s which are a complex task, fault detection and isolation based on SDG method is made by getting initial or steady-state response and propagation through paths which can generate a spurious solutions that can not indicate the actual fault. To overcome these problems, we propose a new fault detection and isolation algorithm.…”

Section: Introductionmentioning

confidence: 99%