Optimal Input Design for Subspace-Based Fault Detection and Identification

Alireza Esna Ashari 1 Laurent Mevel 1
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Abstract : This study focuses on input design for subspace based fault detection and identification methods and investigates its possible advantages over using noise inputs. In several real applications the noise available in environment is the only input to the system and in some cases produce low quality output data for subspace identification and fault detection purposes. Therefore, model order may be underestimated. Due to the nature of subspace based methods, some modes of the system may not appear in the response as the input is not strong enough to excite these modes. In order to improve the result, a method is suggested in literature, that is to use "rotated" input. The rotated input design is proposed in several papers to apply to "ill conditioned systems" in which the vector of different outputs are typically close to collinearity if a white noise is used. In this report, we use this technique to verify possible improvement of subspace-based identification method including output-only, and input-output approaches. Then, for the first time we investigate the possible impacts of the rotated input on subspace base fault detection method. Simulations on a high-purity distillation column shows that this auxiliary input can improve subspace-based fault detection and identification.
Type de document :
[Research Report] RR-7609, INRIA. 2010, pp.20
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Alireza Esna Ashari, Laurent Mevel. Optimal Input Design for Subspace-Based Fault Detection and Identification. [Research Report] RR-7609, INRIA. 2010, pp.20. 〈inria-00589508〉



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