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中南大学学报(英文版)

Journal of Central South University

Vol. 26    No. 9    September 2019

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Fault detection in flotation processes based on deep learning and support vector machine
LI Zhong-mei(李中美)1, GUI Wei-hua(桂卫华)1, ZHU Jian-yong(朱建勇)2

1. School of Automation, Central South University, Changsha 410083, China;
2. School of Electrical and Automation Engineering, East China Jiaotong University,
Nanchang 330013, China

Abstract:Effective fault detection techniques can help flotation plant reduce reagents consumption, increase mineral recovery, and reduce labor intensity. Traditional, online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation, like color, shape, size and texture, always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case. In this work, a new integrated method based on convolution neural network (CNN) combined with transfer learning approach and support vector machine (SVM) is proposed to automatically recognize the flotation condition. To be more specific, CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection. As compared with the existed recognition methods, it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy. Hence, a CNN-SVM based, real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

 

Key words: flotation processes; convolutional neural network; support vector machine; froth images; fault detection

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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