Machinery noise source identification with deep learning

Marko Antila (Corresponding author), Seppo Rantala, Jari Kataja, Lasse Lamula, Heikki Isomoisio, Radoslaw Zimroz, Jacek Wodecki, Agnieszka Wylomanska

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


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.

Original languageEnglish
Title of host publicationInterNoise 19, Madrid, Spain
EditorsAntonio Calvo-Manzano, Ana Delgado, Antonio Perez-Lopez, Jose Salvador Santiago
PublisherInstitute of Noise Control Engineering
Number of pages6
ISBN (Electronic)978-848798531-7
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
Event48th International Congress and Exhibition on Noise Control Engineering, INTER-NOISE 2019 MADRID - Madrid, Spain
Duration: 16 Jun 201919 Jun 2019

Publication series

SeriesNOISE-CON Proceedings


Conference48th International Congress and Exhibition on Noise Control Engineering, INTER-NOISE 2019 MADRID


  • Deep learning
  • Noise ranking
  • Noise sources

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