Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis
Top Cited Papers
- 30 May 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 62 (1), 637-646
- https://doi.org/10.1109/tie.2014.2327589
Abstract
Distinct feature extraction methods are simultaneously used to describe bearing faults. This approach produces a large number of heterogeneous features that augment discriminative information but, at the same time, create irrelevant and redundant information. A subsequent feature selection phase filters out the most discriminative features. The feature models are based on the complex envelope spectrum, statistical time- and frequency-domain parameters, and wavelet packet analysis. Feature selection is achieved by conventional search of the feature space by greedy methods. For the final fault diagnosis, the k-nearest neighbor classifier, feedforward net, and support vector machine are used. Performance criteria are the estimated error rate and the area under the receiver operating characteristic curve (AUC-ROC). Experimental results are shown for the Case Western Reserve University Bearing Data. The main contribution of this paper is the strategy to use several different feature models in a single pool, together with feature selection to optimize the fault diagnosis system. Moreover, robust performance estimation techniques usually not encountered in the context of engineering are employed.Keywords
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