Cross-domain fault diagnosis through optimal transport for a CSTR process

Eduardo Fernandes Montesuma, Michela Mulas, Francesco Corona, Fred Maurice Ngole Mboula

Tutkimustuotos: LehtiartikkeliConference articleScientificvertaisarvioitu

27 Lataukset (Pure)

Abstrakti

Fault diagnosis is a key task for developing safer control systems, especially in chemical plants. Nonetheless, acquiring good labeled fault data involves sampling from dangerous system conditions. A possible workaround to this limitation is to use simulation data for training data-driven fault diagnosis systems. However, due to modelling errors or unknown factors, simulation data may differ in distribution from real-world data. This setting is known as cross-domain fault diagnosis (CDFD). We use optimal transport for: (i) exploring how modelling errors relate to the distance between simulation (source) and real-world (target) data distributions, and (ii) matching source and target distributions through the framework of optimal transport for domain adaptation (OTDA), resulting in new training data that follows the target distribution. Comparisons show that OTDA outperforms other CDFD methods.

AlkuperäiskieliEnglanti
Sivut946-951
Sivumäärä6
JulkaisuIFAC-PapersOnLine
Vuosikerta55
Numero7
DOI - pysyväislinkit
TilaJulkaistu - 5 elok. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIFAC Symposium on Dynamics and Control of Process Systems, including Biosystems - Busan, Etelä-Korea
Kesto: 14 kesäk. 202217 kesäk. 2022
Konferenssinumero: 13

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