2015
DOI: 10.1016/j.mineng.2014.12.006
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Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines

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Cited by 14 publications
(2 citation statements)
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“…Fault diagnosis, i.e., the identification of variables associated with faulty conditions, is usually achieved via the use of approaches based on PCA. Some applications of these methods include flotation [128][129][130] and grinding [126,127]. For example, Wakefield et al [127] simulated a milling circuit for investigating faults related to particle size estimates and mill liners.…”
Section: Artificial Intelligence (Ai) Applied To Multiphase Systemsmentioning
confidence: 99%
“…Fault diagnosis, i.e., the identification of variables associated with faulty conditions, is usually achieved via the use of approaches based on PCA. Some applications of these methods include flotation [128][129][130] and grinding [126,127]. For example, Wakefield et al [127] simulated a milling circuit for investigating faults related to particle size estimates and mill liners.…”
Section: Artificial Intelligence (Ai) Applied To Multiphase Systemsmentioning
confidence: 99%
“…Wang et al 3 developed a method based on Fuzzy Reasoning Spiking Neural P systems, Fuzzy Reasoning Spiking Neural P systems to handle incompleteness and uncertainty in power transmission network fault diagnosis in electric power systems; this approach provided intuitive illustration of graphical models and understandability of diagnosis model-building process. Groenewald and Aldrich 4 proposed a method by means of causality maps and extreme learning machines for root cause analysis to deal with highly nonlinear systems. Tian et al 5 presented a fault diagnosis method based on current kernel density estimation for transistor open-circuit fault using current kernel density estimation, Euclidean distance, fault detection, and isolation to analyze the influence factors of thermic cycling and voltage surge.…”
Section: Introductionmentioning
confidence: 99%