A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy
Hydrometallurgy is a metallurgical method for processing complex ores and low-grade ores while reducing environmental pollution. The density of the thickening process in hydrometallurgical production is rather poor, and there are many interference factors, resulting in frequent failures in the densi...
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doaj-a08fec7a6a83406693477046412741b92021-03-29T23:54:30ZengIEEEIEEE Access2169-35362019-01-01714231714232410.1109/ACCESS.2019.29440298849997A Method of Fault Monitoring and Diagnosis for the Thickener in HydrometallurgyDong Xiao0https://orcid.org/0000-0002-0401-6654Ba Tuan Le1https://orcid.org/0000-0003-2333-1948Zhichao Yu2Chenyi Liu3Hongzong Li4Qifei He5Hongfei Xie6Jichun Wang7College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaComputer Science and Engineering School, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaScience School, Shenyang Jianzhu University, Shenyang, ChinaHydrometallurgy is a metallurgical method for processing complex ores and low-grade ores while reducing environmental pollution. The density of the thickening process in hydrometallurgical production is rather poor, and there are many interference factors, resulting in frequent failures in the density of the thickening process. The main focus of this paper is to propose a method of fault monitoring and diagnosis for the density of the thickening process in hydrometallurgy. First, through the support vector machine (SVM) algorithm, the fault detection model is established to monitor the blockage of the underflow pipeline of the thickener. Second, the fault diagnosis model is established by using the random forest algorithm, and particle swarm optimization is used to optimize the fault diagnosis model. The fault type is judged using the optimized diagnosis model, and the corresponding treatment measures are taken accordingly.https://ieeexplore.ieee.org/document/8849997/Hydrometallurgydensity of thickeningsupport vector machinesparticle swarm optimizationrandom forestsfault monitoring and fault diagnosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dong Xiao Ba Tuan Le Zhichao Yu Chenyi Liu Hongzong Li Qifei He Hongfei Xie Jichun Wang |
spellingShingle |
Dong Xiao Ba Tuan Le Zhichao Yu Chenyi Liu Hongzong Li Qifei He Hongfei Xie Jichun Wang A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy IEEE Access Hydrometallurgy density of thickening support vector machines particle swarm optimization random forests fault monitoring and fault diagnosis |
author_facet |
Dong Xiao Ba Tuan Le Zhichao Yu Chenyi Liu Hongzong Li Qifei He Hongfei Xie Jichun Wang |
author_sort |
Dong Xiao |
title |
A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy |
title_short |
A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy |
title_full |
A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy |
title_fullStr |
A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy |
title_full_unstemmed |
A Method of Fault Monitoring and Diagnosis for the Thickener in Hydrometallurgy |
title_sort |
method of fault monitoring and diagnosis for the thickener in hydrometallurgy |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Hydrometallurgy is a metallurgical method for processing complex ores and low-grade ores while reducing environmental pollution. The density of the thickening process in hydrometallurgical production is rather poor, and there are many interference factors, resulting in frequent failures in the density of the thickening process. The main focus of this paper is to propose a method of fault monitoring and diagnosis for the density of the thickening process in hydrometallurgy. First, through the support vector machine (SVM) algorithm, the fault detection model is established to monitor the blockage of the underflow pipeline of the thickener. Second, the fault diagnosis model is established by using the random forest algorithm, and particle swarm optimization is used to optimize the fault diagnosis model. The fault type is judged using the optimized diagnosis model, and the corresponding treatment measures are taken accordingly. |
topic |
Hydrometallurgy density of thickening support vector machines particle swarm optimization random forests fault monitoring and fault diagnosis |
url |
https://ieeexplore.ieee.org/document/8849997/ |
work_keys_str_mv |
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