Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation-Reference-Cited by-同舟云学术

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Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation

Published:2015-12 Issue: Volume:169 Page:392-402
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Liu Zhuo,Chai Tianyou,Yu Wen,Tang Jian

Funder

National Science Foundation for Post-doctoral Scientists of China

National Natural Science Foundation of China

111 Project

National 863 Project of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference20 articles.

1. Operational optimization and feedback control for complex industrial processes;Chai;Acta Autom. Sin.,2013

2. Monitoring grinding parameters by vibration signal measurement a primary application;Zeng;Miner. Eng.,1994

3. Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell;Tang;Min. Eng.,2010

4. Statistical pattern recognition: a review;Jain;IEEE Trans. Pattern Anal. Mach. Intell.,2000

5. Unsupervised linear feature-extraction Mmethods and their effects in the classification of high-dimensional data;Jiménez-Rodríguez;IEEE Trans. Geosci. Remote Sens.,2007

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2. Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD;Gospodarka Surowcami Mineralnymi - Mineral Resources Management;2023-07-26

3. Heterogeneous selective ensemble learning model for mill load parameters forecasting by using multiscale mechanical frequency spectrum;Soft Computing;2022-10-11

4. Soft measurement of ball mill load under variable working conditions based on deep transfer learning;Measurement Science and Technology;2022-04-21

5. Metodología híbrida para la estimación del nivel de llenado en un molino de bolas;Revista Iberoamericana de Automática e Informática industrial;2021-10-01

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