Fault detection and fault diagnosis are crucial subsystems to be integrated within the control architecture of modern industrial processes to ensure high quality standards. In this paper we present a two-stage unsupervised approach for fault detection and diagnosis in household appliances. In particular a suitable testing procedure has been implemented on a real industrial production line in order to extract the most meaningful features that allow to efficiently classify different types of fault by consecutively exploiting deep autoencoder neural network and k-means or hierarchical clustering techniques.

A deep learning unsupervised approach for fault diagnosis of household appliances / Cordoni, F.; Bacchiega, G.; Bondani, G.; Radu, R.; Muradore, R.. - 53:2(2020), pp. 10749-10754. (Intervento presentato al convegno 21st IFAC World Congress 2020 tenutosi a deu nel 2020) [10.1016/j.ifacol.2020.12.2856].

A deep learning unsupervised approach for fault diagnosis of household appliances

Cordoni F.;
2020-01-01

Abstract

Fault detection and fault diagnosis are crucial subsystems to be integrated within the control architecture of modern industrial processes to ensure high quality standards. In this paper we present a two-stage unsupervised approach for fault detection and diagnosis in household appliances. In particular a suitable testing procedure has been implemented on a real industrial production line in order to extract the most meaningful features that allow to efficiently classify different types of fault by consecutively exploiting deep autoencoder neural network and k-means or hierarchical clustering techniques.
2020
IFAC-PapersOnLine
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
Elsevier B.V.
Cordoni, F.; Bacchiega, G.; Bondani, G.; Radu, R.; Muradore, R.
A deep learning unsupervised approach for fault diagnosis of household appliances / Cordoni, F.; Bacchiega, G.; Bondani, G.; Radu, R.; Muradore, R.. - 53:2(2020), pp. 10749-10754. (Intervento presentato al convegno 21st IFAC World Congress 2020 tenutosi a deu nel 2020) [10.1016/j.ifacol.2020.12.2856].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/322972
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