English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents an autoencoder based unsupervised approach to identifyanomaly in an industrial machine using sounds produced by the machine. Theproposed framework is trained using log-melspectrogram representations of thesound signal. In classification, our hypothesis is that the reconstructionerror computed for an abnormal machine is larger than that of the a normalmachine, since only normal machine sounds are being used to train theautoencoder. A threshold is chosen to discriminate between normal and abnormalmachines. However, the threshold changes as surrounding conditions vary. Toselect an appropriate threshold irrespective of the surrounding, we propose ascene classification framework, which can classify the underlying surrounding.Hence, the threshold can be selected adaptively irrespective of thesurrounding. The experiment evaluation is performed on MIMII dataset forindustrial machines namely fan, pump, valve and slide rail. Our experimentanalysis shows that utilizing adaptive threshold, the performance improvessignificantly as that obtained using the fixed threshold computed for a givensurrounding only.

Health Monitoring of Industrial machines using Scene-Aware Threshold Selection