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DC Field | Value | Language |
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dc.contributor.author | Seera, M | - |
dc.contributor.author | Wong, MLD | - |
dc.contributor.author | Nandi, A | - |
dc.date.accessioned | 2017-07-18T15:48:13Z | - |
dc.date.available | 2017-07-18T15:48:13Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Applied Soft Computing, 57: pp. 427-435, (2017) | en_US |
dc.identifier.issn | 1568-4946 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/14941 | - |
dc.description.abstract | In this paper, classification of ball bearing faults using vibration signals is presented. A review of condition monitoring using vibration signals with various intelligent systems is first presented. A hybrid intelligent model, FMM-RF, consisting of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model, is proposed. A benchmark problem is tested to evaluate the practicality of the FMM-RF model. The proposed model is then applied to a real-world dataset. In both cases, power spectrum and sample entropy features are used for classification. Results from both experiments show good accuracy achieved by the proposed FMM-RF model. In addition, a set of explanatory rules in the form of a decision tree is extracted to justify the predictions. The outcomes indicate the usefulness of FMM-RF in performing classification of ball bearing faults. | en_US |
dc.description.sponsorship | Professor Nandi is a Distinguished Visiting Professor at Tongji University, Shanghai, China. This work was partly supported by the National Science Foundation of China grant number 61520106006 and the National Science Foundation of Shanghai grant number 16JC1401300. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Condition monitoring | en_US |
dc.subject | Ball bearing | en_US |
dc.subject | Electrical motor | en_US |
dc.subject | Fuzzy min-max neural network | en_US |
dc.subject | Random forest | en_US |
dc.title | Classification of ball bearing faults using a hybrid intelligent model | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.asoc.2017.04.034 | - |
dc.relation.isPartOf | Applied Soft Computing | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | 1.24 MB | Adobe PDF | View/Open |
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