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Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Zhang, Kui, Li, Yuhua, Scarf, Philip and Ball, Andrew (2011) Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks. Neurocomputing, 74 (17). pp. 2941-2952. [Journal article]

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URL: http://dx.doi.org/10.1016/j.neucom.2011.03.043

DOI: 10.1016/j.neucom.2011.03.043

Abstract

The technique of machinery fault diagnosis has been greatly enhanced over recent years with the application of many pattern classification methods. However, these classification methods suffer from the “curse of dimensionality” when applied to high-dimensional fault diagnosis data. In order to solve the problem, this paper proposes a hybrid model which combines multiple feature selection models to select the most significant input features from all potentially relevant features. Among the models, eight filter models are used to pre-rank the candidate features. They include data variance, Pearson correlation coefficient, the Relief algorithm, Fisher score, class separability, chi-squared, information gain and gain ratio. These variable ranking models measure features from various perspectives, and lead to different ranking results. Based on the effect of the ranking results on the Radial Basis Function (RBF) classification, a weighted voting scheme is then introduced to re-rank features. Furthermore, two wrapper models, a Binary Search (BS) model and a Sequential Backward Search (SBS) model are utilized to minimize the number of relevant features. To demonstrate the potential for applying the method to machinery fault diagnosis, two case studies are discussed. The experiment results support the conclusion that this method is useful for revealing fault-related frequency features.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:20066
Deposited By:Dr Yuhua Li
Deposited On:23 Sep 2011 14:09
Last Modified:23 Sep 2011 14:09

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