A Deep Learning Approach to the Acoustic Condition Monitoring of a Sintering Plant-Reference-Cited by-同舟云学术

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A Deep Learning Approach to the Acoustic Condition Monitoring of a Sintering Plant

Published:2018-11 Issue: Volume: Page:
ISSN:
Container-title:2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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Author:

Pasha Shahab,Ritz Christian,Stirling David,Zulli Paul,Pinson David,Chew Sheng

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8648538/8659446/08659486.pdf?arnumber=8659486

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Acoustic-Based Machine Condition Monitoring—Methods and Challenges;Eng;2023-01-01

2. Evaluation of the Air Leakage Flowrate in Sintering Processes;Fluid Dynamics & Materials Processing;2023

3. Hierarchical Cavitation Intensity Recognition Using Sub-Master Transition Network-Based Acoustic Signals in Pipeline Systems;2023

4. Current Trends on Deep Learning Techniques Applied in Iron and Steel Making Field: A Review;Tetsu-to-Hagane;2022

5. Machine-learnt Beamforming for Large Aperture 3D Microphone Arrays, An Industrial Application;2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP);2021-10-06

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