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.
Original language | English |
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Title of host publication | Noise control for a better environment |
Subtitle of host publication | The 48th International Congress and Exhibition on Noise Control Engineering - INTERNOISE 2019 |
Editors | Antonio Calvo-Manzano, Ana Delgado, Antonio Perez-Lopez, Jose Salvador Santiago |
Publisher | Sociedad Española de Acústica |
Number of pages | 6 |
ISBN (Print) | 978-84-87985-31-7 |
Publication status | Published - 2019 |
MoE publication type | A4 Article in a conference publication |
Event | 48th International Congress and Exhibition on Noise Control Engineering, INTER-NOISE 2019 - Madrid, Spain Duration: 16 Jun 2019 → 19 Jun 2019 Conference number: 49 |
Publication series
Series | NOISE-CON Proceedings |
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ISSN | 0736-2935 |
Conference
Conference | 48th International Congress and Exhibition on Noise Control Engineering, INTER-NOISE 2019 |
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Abbreviated title | Inter-noise 2019 |
Country/Territory | Spain |
City | Madrid |
Period | 16/06/19 → 19/06/19 |
Keywords
- Deep learning
- Noise ranking
- Noise sources