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

Autor: Peter A. Wallace, K. P. Ramachandran, Maamar Ali Saud ALTobi, David K. Harrison, Geraint Bevan
Rok vydání: 2019
Předmět:
Computer Networks and Communications
Computer science
020209 energy
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Biomaterials
Wavelet
Polynomial kernel
0202 electrical engineering
electronic engineering
information engineering
Continuous wavelet transform
Civil and Structural Engineering
Fluid Flow and Transfer Processes
Artificial neural network
business.industry
Mechanical Engineering
020208 electrical & electronic engineering
Metals and Alloys
Pattern recognition
ComputerSystemsOrganization_PROCESSORARCHITECTURES
Centrifugal pump
Perceptron
Backpropagation
Electronic
Optical and Magnetic Materials
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
lcsh:TA1-2040
Hardware and Architecture
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
Zdroj: Engineering Science and Technology, an International Journal, Vol 22, Iss 3, Pp 854-861 (2019)
ISSN: 2215-0986
DOI: 10.1016/j.jestch.2019.01.005
Popis: 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)
Databáze: OpenAIRE
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