A deep learning unsupervised approach for fault diagnosis of household appliances

A deep learning unsupervised approach for fault diagnosis of household appliances

IRS has developed in collaboration with the University of Verona a new methodology for the test of household appliances based on the new artificial intelligence techniques: the deep learning.
The principles on which this new technique is based are been presented at the IFAC International Conference, held in Germany between 11/17 July 2020. They were published with the title “A deep learning unsupervised approach for fault diagnosis of household appliances”.

Fault detection and fault diagnosis are crucial subsystems to be integrated within the control architecture of modern industrial processes to ensure high quality standards. We presented 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 hierarchical clustering techniques.
These methodologies have been developed within the regional project “PreMANI” – Predictive Manufacturing: design, development and implementation of Digital Manufacturing solutions for the forecast of Quality and Intelligent Maintenance “and currently under development at the Electrolux factory in Susegana

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