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.
|Titolo:||A deep learning unsupervised approach for fault diagnosis of household appliances|
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||04.01 Contributo in atti di convegno|