In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem, which is addressed in many cases via Novelty Detection (ND): an observation is classified as novel if it differs significantly from reference, healthy data. ND is practically implemented summarizing a multivariate dataset with univariate distance information called Novelty Index. As many different approaches are possible to produce NIs, in this analysis, the possibility of implementing a simple classifier in a reduced-dimensionality space of NIs is studied. In addition to a simple decision-tree-like classification method, the process for obtaining the NIs can result as a dimension reduction method and, in turn, the NIs can be used for other classification algorithms. In addition, a case study will be analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society, on the occasion of the Data Challenge 2021.

From Novelty Detection to a Genetic Algorithm Optimized Classification for the Diagnosis of a SCADA-Equipped Complex Machine / Viale, Luca; Daga, Alessandro Paolo; Fasana, Alessandro; Garibaldi, Luigi. - In: MACHINES. - ISSN 2075-1702. - 10:4(2022), p. 270. [10.3390/machines10040270]

From Novelty Detection to a Genetic Algorithm Optimized Classification for the Diagnosis of a SCADA-Equipped Complex Machine

Viale, Luca;Daga, Alessandro Paolo;Fasana, Alessandro;Garibaldi, Luigi
2022

Abstract

In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem, which is addressed in many cases via Novelty Detection (ND): an observation is classified as novel if it differs significantly from reference, healthy data. ND is practically implemented summarizing a multivariate dataset with univariate distance information called Novelty Index. As many different approaches are possible to produce NIs, in this analysis, the possibility of implementing a simple classifier in a reduced-dimensionality space of NIs is studied. In addition to a simple decision-tree-like classification method, the process for obtaining the NIs can result as a dimension reduction method and, in turn, the NIs can be used for other classification algorithms. In addition, a case study will be analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society, on the occasion of the Data Challenge 2021.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2960924