An Efficient Polynomial Chaos Expansion Strategy for Active Fault Identification of Chemical Processes. Schenkendorf, R., Xie, X., & Krewer, U. In Computer Aided Chemical Engineering, volume 40, pages 1675–1680. 2017.
An Efficient Polynomial Chaos Expansion Strategy for Active Fault Identification of Chemical Processes [link]Paper  doi  abstract   bibtex   
To gain profit from complex chemical processes, it is essential to ensure its proper operation, i.e. to avoid costly unexpected downtimes of underlying processing units. This paper explores a highly efficient active fault detection and isolation (FDI) framework, which facilitates the discriminability of a set of analysed model candidates including the reference model (nominal behaviour) as well as pre-defined failure models (faulty behaviour). Practically, an auxiliary, model-discriminating input is derived by solving a dynamic optimization problem. While using a model-based approach, the active FDI implementation has to be robustified against the inherent model parameter uncertainties. To this end, a non-intrusive polynomial chaos expansion (PCE) is used to address these uncertainties. To guarantee a computationally feasible performance, the original PCE setting has been considerably improved. Here, the basic idea is to render the design variables (auxiliary inputs) into random variables as well. Thus, the derived PCE results are not only sensitive to the model parameters but also to the design variables. To lower the computational burden further, a least angle regression strategy is applied utilizing the sparsity property of the PCE approach. The overall effectiveness of this One-Short Sparse Polynomial Chaos Expansion (OS2-PCE) concept for FDI is illustrated conceptually by analysing a tubular plug flow reactor.
@incollection{schenkendorf_efficient_2017,
	title = {An {Efficient} {Polynomial} {Chaos} {Expansion} {Strategy} for {Active} {Fault} {Identification} of {Chemical} {Processes}},
	volume = {40},
	copyright = {All rights reserved},
	url = {https://linkinghub.elsevier.com/retrieve/pii/B9780444639653502816},
	abstract = {To gain profit from complex chemical processes, it is essential to ensure its proper operation, i.e. to avoid costly unexpected downtimes of underlying processing units. This paper explores a highly efficient active fault detection and isolation (FDI) framework, which facilitates the discriminability of a set of analysed model candidates including the reference model (nominal behaviour) as well as pre-defined failure models (faulty behaviour). Practically, an auxiliary, model-discriminating input is derived by solving a dynamic optimization problem. While using a model-based approach, the active FDI implementation has to be robustified against the inherent model parameter uncertainties. To this end, a non-intrusive polynomial chaos expansion (PCE) is used to address these uncertainties. To guarantee a computationally feasible performance, the original PCE setting has been considerably improved. Here, the basic idea is to render the design variables (auxiliary inputs) into random variables as well. Thus, the derived PCE results are not only sensitive to the model parameters but also to the design variables. To lower the computational burden further, a least angle regression strategy is applied utilizing the sparsity property of the PCE approach. The overall effectiveness of this One-Short Sparse Polynomial Chaos Expansion (OS2-PCE) concept for FDI is illustrated conceptually by analysing a tubular plug flow reactor.},
	booktitle = {Computer {Aided} {Chemical} {Engineering}},
	author = {Schenkendorf, René and Xie, Xiangzhong and Krewer, Ulrike},
	year = {2017},
	doi = {10.1016/B978-0-444-63965-3.50281-6},
	keywords = {Active Fault Detection and Isolation, Dynamic Optimization, Least Angle Regression, Polynomial Chaos Expansion},
	pages = {1675--1680},
}

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