Performance comparison between FFT-based segmentation, feature selection and fault identification algorithm and neural network for the condition monitoring of centrifugal equipment

This paper compares and evaluates the performance of two major feature selection and fault identification methods utilized for the Condition Monitoring (CM) of centrifugal equipment, namely FFT-based Segmentation, Feature Selection, and Fault Identification (FS2FI) algorithm and Neural Network (NN)....

Full description

Bibliographic Details

Main Authors:	Samer S.A.A. Gowid, Roger Dixon, Saud Ghani
Format:	Default Article
Published:	2017
Subjects:	Mechanical engineering not elsewhere classified Condition based monitoring Condition monitoring Feature selection Neural network Centrifugal equipment Fault detection FFT Mechanical Engineering not elsewhere classified
Online Access:	https://hdl.handle.net/2134/23657
Tags:	Add Tag No Tags, Be the first to tag this record!