Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks
(2011)
Journal Article
Zhang, K., Li, Y., Scarf, P., & Ball, A. (2011). Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks. Neurocomputing, 74(17), 2941-2952. https://doi.org/10.1016/j.neucom.2011.03.043
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-d... Read More about Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks.