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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione