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

journal contribution
posted on 10.01.2017, 13:44 by Samer S.A.A. Gowid, Roger Dixon, Saud Ghani
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). Mutli-Layer Perceptron is the most commonly used NN model for fault pattern recognition. Feature-selection and Trending play an important role in pattern recognition, and hence, affect the performance of CM systems. The technical and developmental challenges of both methods were investigated experimentally on a Paxton industrial centrifugal air blower system with a rotational speed of 15,650 RPM‟s. Five different machine conditions were experimentally emulated in the laboratory. A low training-to-testing ratio of 50% was utilized to evaluate the performance of both methods. In order to maximize fault identification accuracy and minimize computing time and cost, a near-optimal NN configuration was identified. The results showed that both techniques operated with a fault identification accuracy of 100%. However, the FS2FI algorithm showed a number of advantages over NN. These advantages include the ease of implementation and a reduction of cost and time in development and computing, as it processed the data from the first trial in less than 6.2% of the time taken by the neural network.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Dynamic Systems, Measurement, and Control

Citation

GOWID, S., DIXON, R. and GHANI, S., 2017. Performance comparison between FFT-based segmentation, feature selection and fault identification algorithm and neural network for the condition monitoring of centrifugal equipment. Journal of Dynamic Systems, Measurement, and Control, 139 (6), 061013.

Publisher

© American Society of Mechanical Engineers (ASME)

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

15/11/2016

Publication date

2017

Notes

This paper is in closed access.

ISSN

0022-0434

eISSN

1528-9028

Language

en

Exports