Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system

Condition diagnosis of critical system such as multiple-bearing system is one of the most important maintenance activities in industry because it is essential that faults are detected early before the performance of the whole system is affected. Currently, the most significant issues in condition di...

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Main Author:	Wulandhari, Lili Ayu
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/78154/1/LiliAyuWulandhariPFC2014.pdf
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spelling	my-utm-ep.781542018-07-25T07:57:43Z Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system 2014-07 Wulandhari, Lili Ayu QA75 Electronic computers. Computer science Condition diagnosis of critical system such as multiple-bearing system is one of the most important maintenance activities in industry because it is essential that faults are detected early before the performance of the whole system is affected. Currently, the most significant issues in condition diagnosis are how to improve accuracy and stability of accuracy, as well as lessen the complexity of the diagnosis which would reduce processing time. Researchers have developed diagnosis techniques based on metaheuristic, specifically, Back Propagation Neural Network (BPNN) for single bearing system and small numbers of condition classes. However, they are not directly applicable or effective for multiple-bearing system because the diagnosis accuracy achieved is unsatisfactory. Therefore, this research proposed hybrid techniques to improve the performance of BPNN in terms of accuracy and stability of accuracy by using Adaptive Genetic Algorithm and Back Propagation Neural Network (AGA-BPNN), and multiple BPNN with AGA-BPNN (mBPNNAGA- BPNN). These techniques are tested and validated on vibration signal data of multiple-bearing system. Experimental results showed the proposed techniques outperformed the BPPN in condition diagnosis. However, the large number of features from multiple-bearing system has affected the complexity of AGA-BPNN and mBPNN-AGA-BPNN, and significantly increased the amount of required processing time. Thus to investigate further, whether the number of features required can be reduced without compromising the diagnosis accuracy and stability, Grey Relational Analysis (GRA) was applied to determine the most dominant features in reducing the complexity of the diagnosis techniques. The experimental results showed that the hybrid of GRA and mBPNN-AGA-BPNN achieved accuracies of 99% for training, 100% for validation and 100% for testing. Besides that, the performance of the proposed hybrid accuracy increased by 11.9%, 13.5% and 11.9% in training, validation and testing respectively when compared to the standard BPNN. This hybrid has lessened the complexity which reduced nearly 55.96% of processing time. Furthermore, the hybrid has improved the stability of the accuracy whereby the differences in accuracy between the maximum and minimum values were 0.2%, 0% and 0% for training, validation and testing respectively. Hence, it can be concluded that the proposed diagnosis techniques have improved the accuracy and stability of accuracy within the minimum complexity and significantly reduced processing time. 2014-07 Thesis http://eprints.utm.my/id/eprint/78154/ http://eprints.utm.my/id/eprint/78154/1/LiliAyuWulandhariPFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97947 phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Wulandhari, Lili Ayu Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
description	Condition diagnosis of critical system such as multiple-bearing system is one of the most important maintenance activities in industry because it is essential that faults are detected early before the performance of the whole system is affected. Currently, the most significant issues in condition diagnosis are how to improve accuracy and stability of accuracy, as well as lessen the complexity of the diagnosis which would reduce processing time. Researchers have developed diagnosis techniques based on metaheuristic, specifically, Back Propagation Neural Network (BPNN) for single bearing system and small numbers of condition classes. However, they are not directly applicable or effective for multiple-bearing system because the diagnosis accuracy achieved is unsatisfactory. Therefore, this research proposed hybrid techniques to improve the performance of BPNN in terms of accuracy and stability of accuracy by using Adaptive Genetic Algorithm and Back Propagation Neural Network (AGA-BPNN), and multiple BPNN with AGA-BPNN (mBPNNAGA- BPNN). These techniques are tested and validated on vibration signal data of multiple-bearing system. Experimental results showed the proposed techniques outperformed the BPPN in condition diagnosis. However, the large number of features from multiple-bearing system has affected the complexity of AGA-BPNN and mBPNN-AGA-BPNN, and significantly increased the amount of required processing time. Thus to investigate further, whether the number of features required can be reduced without compromising the diagnosis accuracy and stability, Grey Relational Analysis (GRA) was applied to determine the most dominant features in reducing the complexity of the diagnosis techniques. The experimental results showed that the hybrid of GRA and mBPNN-AGA-BPNN achieved accuracies of 99% for training, 100% for validation and 100% for testing. Besides that, the performance of the proposed hybrid accuracy increased by 11.9%, 13.5% and 11.9% in training, validation and testing respectively when compared to the standard BPNN. This hybrid has lessened the complexity which reduced nearly 55.96% of processing time. Furthermore, the hybrid has improved the stability of the accuracy whereby the differences in accuracy between the maximum and minimum values were 0.2%, 0% and 0% for training, validation and testing respectively. Hence, it can be concluded that the proposed diagnosis techniques have improved the accuracy and stability of accuracy within the minimum complexity and significantly reduced processing time.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Wulandhari, Lili Ayu
author_facet	Wulandhari, Lili Ayu
author_sort	Wulandhari, Lili Ayu
title	Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
title_short	Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
title_full	Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
title_fullStr	Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
title_full_unstemmed	Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
title_sort	enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/78154/1/LiliAyuWulandhariPFC2014.pdf
_version_	1747817920233209856

Enhanced genetic algorithm-based back propagation neural network to diagnose conditions of multiple-bearing system

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