Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/14941
Title: Classification of ball bearing faults using a hybrid intelligent model
Authors: Seera, M
Wong, MLD
Nandi, A
Keywords: Condition monitoring;Ball bearing;Electrical motor;Fuzzy min-max neural network;Random forest
Issue Date: 2017
Publisher: Elsevier
Citation: Applied Soft Computing, 57: pp. 427-435, (2017)
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.
URI: http://bura.brunel.ac.uk/handle/2438/14941
DOI: https://doi.org/10.1016/j.asoc.2017.04.034
ISSN: 1568-4946
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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