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 fast Fourier transform (FFT)-based segmentation, feature selection, and fault identification (FS2FI) algorithm and neural network (NN). Multilayer perceptron (MLP) 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 RPMs. 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 NN.
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June 2017
Research-Article
Performance Comparison Between Fast Fourier Transform-Based Segmentation, Feature Selection, and Fault Identification Algorithm and Neural Network for the Condition Monitoring of Centrifugal Equipment
Samer Gowid,
Samer Gowid
Faculty of Engineering,
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK;
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK;
Department of Mechanical and
Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
e-mail: samer@qu.edu.qa
Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
e-mail: samer@qu.edu.qa
Search for other works by this author on:
Roger Dixon,
Roger Dixon
Faculty of Engineering,
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK
Search for other works by this author on:
Saud Ghani
Saud Ghani
Department of Mechanical
and Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
and Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
Search for other works by this author on:
Samer Gowid
Faculty of Engineering,
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK;
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK;
Department of Mechanical and
Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
e-mail: samer@qu.edu.qa
Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
e-mail: samer@qu.edu.qa
Roger Dixon
Faculty of Engineering,
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK
School of Electronic, Electrical and
Systems Engineering,
Loughborough University,
Leicestershire LE11 3TU, UK
Saud Ghani
Department of Mechanical
and Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
and Industrial Engineering,
College of Engineering,
Qatar University,
Doha 2713, Qatar
1Corresponding author.
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 4, 2016; final manuscript received December 6, 2016; published online April 13, 2017. Assoc. Editor: M. Porfiri.
J. Dyn. Sys., Meas., Control. Jun 2017, 139(6): 061013 (9 pages)
Published Online: April 13, 2017
Article history
Received:
January 4, 2016
Revised:
December 6, 2016
Citation
Gowid, S., Dixon, R., and Ghani, S. (April 13, 2017). "Performance Comparison Between Fast Fourier Transform-Based Segmentation, Feature Selection, and Fault Identification Algorithm and Neural Network for the Condition Monitoring of Centrifugal Equipment." ASME. J. Dyn. Sys., Meas., Control. June 2017; 139(6): 061013. https://doi.org/10.1115/1.4035458
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