Conference
Feature Ranking under Industrial Constraints in Continuous Monitoring Applications based on Machine Learning Techniques
| Τίτλος: | Feature Ranking under Industrial Constraints in Continuous Monitoring Applications based on Machine Learning Techniques |
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| Συγγραφείς: | Bodo, Roberto, Bertocco, Matteo, Bianchi, Alberto |
| Συνεισφορές: | Bodo, Roberto, Bertocco, Matteo, Bianchi, Alberto |
| Στοιχεία εκδότη: | IEEE |
| Έτος έκδοσης: | 2020 |
| Συλλογή: | Padua Research Archive (IRIS - Università degli Studi di Padova) |
| Θεματικοί όροι: | Condition Monitoring Predictive Maintenance Machine Learning Feature engineering Feature selection Industrial constraint Constrained optimisation Data Fusion IoT IIoT Industry 4.0 |
| Περιγραφή: | The design work-flow of machine learning techniques for continuous monitoring or predictive maintenance in an industrial context is usually a two step procedure: the selection of features to be computed from the observed signals and training of a suitable algorithm with real-life meaningful data, that will be next deployed in the second step. Feature selection is a relevant task since it provides a powerful optimisation of the deployed algorithm performance, for the given training data-set. The paper provides a method for feature ranking and selection that embeds constraints coming from real-life applications, including sensing device specifications, environmental noise, available processing resources, being all these latter aspects not considered in the currently available literature methods for feature selection. A practical case-study in the field on anomaly detection of machines is reported and discussed, in order to show the good properties of the provided method. |
| Τύπος εγγράφου: | conference object |
| Περιγραφή αρχείου: | ELETTRONICO |
| Γλώσσα: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/isbn/9781728144603; ispartofbook:Proceedings of 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); firstpage:1; lastpage:6; numberofpages:6; https://hdl.handle.net/11577/3345544; https://ieeexplore.ieee.org/document/9129595 |
| DOI: | 10.1109/i2mtc43012.2020.9129595 |
| Διαθεσιμότητα: | https://hdl.handle.net/11577/3345544 https://doi.org/10.1109/i2mtc43012.2020.9129595 https://ieeexplore.ieee.org/document/9129595 |
| Αριθμός Καταχώρησης: | edsbas.22135E15 |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.1109/i2mtc43012.2020.9129595 |
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