Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT

Maamar  Ali Saud ALTobi; Geraint Bevan; Peter Wallace; David Harrison; K. P. Ramachandran

doi:10.1016/j.jestch.2019.01.005

Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT

Maamar Ali Saud ALTobi^*, Geraint Bevan, Peter Wallace, David Harrison, K. P. Ramachandran

^*Corresponding author for this work

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

101 Citations (Scopus)

227 Downloads (Pure)

Abstract

This paper presents a comparative study of Multilayer Feedforward Perceptron Neural Network which is trained with Back Propagation (MLP-BP) and also using hybrid training using Genetic Algorithm (GA) (MLP-GABP), and Support Vector Machine (SVM) classifiers to classify the fault conditions of a centrifugal pump. Continuous Wavelet Transform (CWT) with three different wavelet functions (Morlet, db8 and rbio1.5) is used to extract the features. GA is also used to optimize the number of hidden layers and neurons of MLP. From the results obtained, MLP-BP has shown better performance than MLP-GABP and SVM using a lower number of features. SVM has performed better using polynomial kernel function using a smaller number of features and parameters. A centrifugal pump test rig has been specifically designed and built for this work in order to create the desired faults.

Original language	English
Pages (from-to)	854-861
Number of pages	8
Journal	Engineering Science and Technology, an International Journal
Volume	22
Issue number	3
Early online date	22 Jan 2019
DOIs	https://doi.org/10.1016/j.jestch.2019.01.005
Publication status	Published - Jun 2019

Keywords

genetic algorithm (GA)
multilayer feedforward perceptron (MLP)
support vector machine (SVM)
continuous wavelet transform (CWT)

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanical Engineering
Metals and Alloys
Fluid Flow and Transfer Processes
Hardware and Architecture
Computer Networks and Communications
Biomaterials
Civil and Structural Engineering

Documents and Links

10.1016/j.jestch.2019.01.005

Saud Al Tobi, A. et al (2019) Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT
2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Final published version, 1.44 MBLicence: CC BY-NC-ND

101 Citations
5 Article
1 Book
1 Chapter (peer-reviewed)

Artificial Intelligence Methods for Fault Diagnosis in Centrifugal Pumps
Ali Saud Al Tobi, M., Bevan, G. & Okedu, K., Jul 2022, American Institute of Physics. 302 p.
Research output: Book/Report › Book
Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump
Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Okedu, K. E., Jun 2022, In: Energy Science & Engineering. 10, 6, p. 1826-1839 14 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
8 Citations (Scopus)

157 Downloads (Pure)
Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform based feature extraction
Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Okedo, K. E., Feb 2021, In: Computational Intelligence. 37, 1, p. 21-46 26 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
18 Citations (Scopus)

269 Downloads (Pure)

Geraint Bevan
- ag090408gcu.acuk
- Department of Electrical and Electronic Engineering - Head of Department
Person
Peter Wallace
- pwagcu.acuk
- Instrumentation and Control - Senior Lecturer
Person

Cite this

@article{0fbec0fb20a349bab86e16a66a51ccb5,

title = "Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT",

abstract = "This paper presents a comparative study of Multilayer Feedforward Perceptron Neural Network which is trained with Back Propagation (MLP-BP) and also using hybrid training using Genetic Algorithm (GA) (MLP-GABP), and Support Vector Machine (SVM) classifiers to classify the fault conditions of a centrifugal pump. Continuous Wavelet Transform (CWT) with three different wavelet functions (Morlet, db8 and rbio1.5) is used to extract the features. GA is also used to optimize the number of hidden layers and neurons of MLP. From the results obtained, MLP-BP has shown better performance than MLP-GABP and SVM using a lower number of features. SVM has performed better using polynomial kernel function using a smaller number of features and parameters. A centrifugal pump test rig has been specifically designed and built for this work in order to create the desired faults.",

keywords = "genetic algorithm (GA), multilayer feedforward perceptron (MLP), support vector machine (SVM), continuous wavelet transform (CWT)",

author = "{Ali Saud ALTobi}, Maamar and Geraint Bevan and Peter Wallace and David Harrison and Ramachandran, {K. P.}",

note = "Acceptance from webpage Fwd to 'for validation' in July 2019 by author. OA article Applied 'no exception' as licence doesn't meet our Gold OA definition. ET 18/12/19 ",

year = "2019",

month = jun,

doi = "10.1016/j.jestch.2019.01.005",

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AU - Ali Saud ALTobi, Maamar

AU - Bevan, Geraint

AU - Wallace, Peter

AU - Harrison, David

AU - Ramachandran, K. P.

N1 - Acceptance from webpage Fwd to 'for validation' in July 2019 by author. OA article Applied 'no exception' as licence doesn't meet our Gold OA definition. ET 18/12/19

PY - 2019/6

Y1 - 2019/6

N2 - This paper presents a comparative study of Multilayer Feedforward Perceptron Neural Network which is trained with Back Propagation (MLP-BP) and also using hybrid training using Genetic Algorithm (GA) (MLP-GABP), and Support Vector Machine (SVM) classifiers to classify the fault conditions of a centrifugal pump. Continuous Wavelet Transform (CWT) with three different wavelet functions (Morlet, db8 and rbio1.5) is used to extract the features. GA is also used to optimize the number of hidden layers and neurons of MLP. From the results obtained, MLP-BP has shown better performance than MLP-GABP and SVM using a lower number of features. SVM has performed better using polynomial kernel function using a smaller number of features and parameters. A centrifugal pump test rig has been specifically designed and built for this work in order to create the desired faults.

AB - This paper presents a comparative study of Multilayer Feedforward Perceptron Neural Network which is trained with Back Propagation (MLP-BP) and also using hybrid training using Genetic Algorithm (GA) (MLP-GABP), and Support Vector Machine (SVM) classifiers to classify the fault conditions of a centrifugal pump. Continuous Wavelet Transform (CWT) with three different wavelet functions (Morlet, db8 and rbio1.5) is used to extract the features. GA is also used to optimize the number of hidden layers and neurons of MLP. From the results obtained, MLP-BP has shown better performance than MLP-GABP and SVM using a lower number of features. SVM has performed better using polynomial kernel function using a smaller number of features and parameters. A centrifugal pump test rig has been specifically designed and built for this work in order to create the desired faults.

KW - genetic algorithm (GA)

KW - multilayer feedforward perceptron (MLP)

KW - support vector machine (SVM)

KW - continuous wavelet transform (CWT)

U2 - 10.1016/j.jestch.2019.01.005

DO - 10.1016/j.jestch.2019.01.005

M3 - Article

SN - 2215-0986

VL - 22

SP - 854

EP - 861

JO - Engineering Science and Technology, an International Journal

JF - Engineering Science and Technology, an International Journal

IS - 3

ER -

Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT

Abstract

Keywords

ASJC Scopus subject areas

Documents and Links

Fingerprint

Research output

Artificial Intelligence Methods for Fault Diagnosis in Centrifugal Pumps

Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump

Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform based feature extraction

Profiles

Geraint Bevan

Peter Wallace

Cite this