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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

    Access Status
    Fulltext not available
    Authors
    Groenewald, J.
    Aldrich, Chris
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Groenewald, J. and Aldrich, C. 2015. Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines. Minerals Engineering. 74: pp. 30-40.
    Source Title
    MINERALS ENGINEERING
    DOI
    10.1016/j.mineng.2014.12.006
    ISSN
    0892-6875
    School
    Dept of Mining Eng & Metallurgical Eng
    URI
    http://hdl.handle.net/20.500.11937/29071
    Collection
    • Curtin Research Publications
    Abstract

    Model-based fault diagnosis tends to be too expensive or time-consuming to apply in the mineral processing industries, owing to the complexity and variability of operations. In contrast, data-based methods are inexpensive, but do not exploit the availability of first principle knowledge of plant operations. In this investigation, the use of process causality maps in conjunction with data-based fault diagnosis is considered as a hybrid methodology that can leverage the advantages of both approaches. Extreme learning machine algorithms are used to implement the data-based component of the approach. These algorithms can be deployed rapidly on large-scale systems and have the ability to deal with highly nonlinear systems. Two different variants are considered, viz. one used in combination with principal component analysis, as well as one with a bagging algorithm for fault diagnosis and applied to an industrial concentrator circuit in South Africa. The use of process causality maps led to significantly more effective fault diagnosis, while the use of extreme learning machines in combination with principal component analysis likewise allowed markedly better fault detection and diagnosis. In contrast, fault diagnosis with the bagging approach did not perform particularly well, owing to the high degree of correlation between the variables, which made it difficult to isolate individual causal variables.

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