Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines
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 ope...
| Main Authors: | , |
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| Format: | Journal Article |
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PERGAMON-ELSEVIER SCIENCE LTD
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/29071 |
| _version_ | 1848752705166639104 |
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| author | Groenewald, J. Aldrich, Chris |
| author_facet | Groenewald, J. Aldrich, Chris |
| author_sort | Groenewald, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:12:52Z |
| format | Journal Article |
| id | curtin-20.500.11937-29071 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:12:52Z |
| publishDate | 2015 |
| publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-290712017-09-13T15:23:30Z Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines Groenewald, J. Aldrich, Chris 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/29071 10.1016/j.mineng.2014.12.006 PERGAMON-ELSEVIER SCIENCE LTD restricted |
| spellingShingle | Groenewald, J. Aldrich, Chris Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| title | Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| title_full | Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| title_fullStr | Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| title_full_unstemmed | Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| title_short | Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| title_sort | root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines |
| url | http://hdl.handle.net/20.500.11937/29071 |