Fault detection in flotation processes based on deep learning and support vector machine-Reference-Cited by-同舟云学术

Fault detection in flotation processes based on deep learning and support vector machine

Published:2019-09 Issue:9 Volume:26 Page:2504-2515
ISSN:2095-2899
Container-title:Journal of Central South University
language:en
Short-container-title:J. Cent. South Univ.

Author:

Li Zhong-mei,Gui Wei-hua^ORCID,Zhu Jian-yong

Publisher

Springer Science and Business Media LLC

Subject

Metals and Alloys,General Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11771-019-4190-8.pdf

Reference22 articles.

1. YANG Chun-hua, ZHOU Kai-jun, MOU Xue-min, GUI Wei-hua. Froth color and size measurement method for flotation based on computer vision [J]. Chinese Journal of Scientific Instrument, 2009, 30(4): 717–721. (in Chinese)

2. XU Can-hui, GUI Wei-hua, YANG Chun-hua, ZHU Hong-qiu, LIN Yi-qiu, SHI Cao. Flotation process fault detection using output PDF of bubble size distribution [J]. Minerals Engineering, 2012, 26: 5–12.

3. ZHAO Lu, PENG Tao, HAN Hua, CAO Wei, LOU Yang-ge, XIE Xiao-tian. Fault condition recognition based on multi-scale co-occurrence matrix for copper flotation process [C]// Proceedings of the 19th World Congress International Federation of Automatic Control. Cape Town, South Africa. 2014: 7091–7097.

4. ALDRICH C, MARAIS C, SHEAN B J, CILLIERS J J. Online monitoring and control of froth flotation systems with machine vision: A review [J]. International Journal of Mineral Processing, 2010, 96(1): 1–13.

5. YANG Chun-hua, XU Can-hui, GUI Wei-hua, DU Jian-jiang. Nonparametric density estimation of bubble size distribution for monitoring mineral flotation process [C]// Proceedings of the 28th Chinese Control Conference. Shanghai, China. 2009: 2941–2945.

Cited by 34 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. Prediction of ash content in coal slime flotation based on CNN-BP method with residual estimation;International Journal of Coal Preparation and Utilization;2024-03-20

2. Abnormal state prediction of flotation process based on dual attention mechanism and multivariate information fusion;The Canadian Journal of Chemical Engineering;2024-02-29

3. Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate;Physicochemical Problems of Mineral Processing;2023-07-26

4. Prediction of Froth Flotation Performance Using Convolutional Neural Networks;Mining, Metallurgy & Exploration;2023-05-05

5. Overview of Bearing Fault Diagnosis Based on Deep Learning;2023 IEEE 3rd International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB);2023-04-14