@article {Antila:2019:0736-2935:4435,
title = "Machinery noise source identification with deep learning",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2019",
volume = "259",
number = "5",
publication date ="2019-09-30T00:00:00",
pages = "4435-4440",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2019/00000259/00000005/art00045",
author = "Antila, Marko and Rantala, Seppo and Kataja, Jari and Lamula, Lasse and Isomoisio, Heikki and Zimroz, Radosaw and Wodecki, Jacek and Wyomaska, Agnieszka",
abstract = "Machinery noise is produced by several noise sources. Our study focuses on hard rock mining machinery in harsh conditions. In such machinery, there are several independent and inter-dependent noise sources. They include cutting, airflow, drilling, and auxiliary devices such as hydraulics.
In this study, we isolate the most annoying or harmful noise sources. This is done by automatic ranking of the noise sources. For the automatic ranking of the noise sources, we have tried deep learning, independent component analysis, and principal component analysis. Deep learning has produced
the best results. It is used widely in detection of noise sources, but it has not been commonly applied to machinery noise source detection. The results show the possibility to separate the noise sources, if adequately long datasets without excessive random and impulsive components are available.
A priori information about the individual noise sources improves the capabilities of the deep learning method, as well. The challenge is to use real-world datasets with slightly corrupted contents successfully and with adequate accuracy to improve the machinery noise properties.",
}