Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy
| Autor: | Luca Puggini, Seán McLoone |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Dimensionality reduction Feature selection 02 engineering and technology Fault Detection Explained variation External Data Representation computer.software_genre 020901 industrial engineering & automation Semiconductors Control and Systems Engineering 020204 information systems OC-SVM Principal component analysis 0202 electrical engineering electronic engineering information engineering OES Spectrum Anomaly detection Data mining Anomaly (physics) computer Dimensionality Reduction Curse of dimensionality Mathematics |
| Zdroj: | Puggini, L & McLoone, S 2016, ' Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy ', IFAC-PapersOnLine, vol. 49, no. 5, pp. 132-137 . https://doi.org/10.1016/j.ifacol.2016.07.102 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2016.07.102 |
| Popis: | To maintain the pace of development set by Moore’s law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data. |
| Databáze: | OpenAIRE |
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