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

Francesco Cordoni;Riccardo Muradore
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
Fault detection and isolation, Deep Learning, Neural networks, Unsupervised Learning, Autoencoder Neural Networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1015036
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