Archives > Volume 12 | Number 4 | December 2017 | > pp 321–336
Advances in Production Engineering & Management
Volume 12 | Number 4 | December 2017 | pp 321–336
An integrated generalized discriminant analysis method and chemical reaction support vector machine model (GDA-CRSVM) for bearing fault diagnosis
Nguyen, V.H.; Cheng, J.S.; Thai, V.T.
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A B S T R A C T
An expert technique in bearing fault diagnosis is proposed for the identification of actual status. A new diagnosis method based on a two-stage hybrid modality in integrating generalized discriminant analysis (GDA) with the chemical reaction support vector machine (CRSVM) classification model, named GDA-CRSVM, is proposed. The GDA reduces high-dimensional data to a more compact data, which serves an optimized CRSVM classification model with input data, in which a support vector machine (SVM) classifier model with the best parameters are selected by the meta-heuristic chemical reaction optimization algorithm (CRO) to build an optimized CRSVM classification model. The implementation of the new proposed method is based on a multi-aspect feature (MAF) set that presents most of the actual aspects of the complex vibration signal. The MAF set is collected from the statistical features in time-domain, frequency-domain, and time-frequency domain features are extracted by local characteristic-scale decomposition (LCD). Experiments have been conducted on two bearing vibration datasets by the expert technique in the bearing fault diagnosis. Results shown that the effectiveness of GDA-CRSVM in terms of classification accuracy and execution time.
A R T I C L E I N F O
Keywords • Bearing fault, Expert fault diagnosis technique, Chemical reaction support vector machine (CRSVM), Multi-aspect feature set, Generalized discriminant analysis (GDA)
Corresponding author • Nguyen, V.H.
Article history • Received 8 June 2017, Revised 23 October 2017, Accepted 26 October 2017
Published on-line • 10 December 2017
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