Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring

Condition monitoring is a process of assessing the health status of a system, process, or machine. Monitoring and identifying any potential fault can be conducted by leveraging measurements from the installed sensors that provide information on the state of the system. In this respect, machine learn...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي: Chang, Timothy Zhi Wei
التنسيق: أطروحة
منشور في: 2023
الموضوعات:
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record_format uketd_dc
spelling my-mmu-ep.128722024-08-28T09:03:11Z Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring 2023-02 Chang, Timothy Zhi Wei Q300-390 Cybernetics Condition monitoring is a process of assessing the health status of a system, process, or machine. Monitoring and identifying any potential fault can be conducted by leveraging measurements from the installed sensors that provide information on the state of the system. In this respect, machine learning models are useful for processing and analysing the sensor data for fault detection. However, the imbalanced nature of these sensory data can cause misleading high accuracy scores. In this research, the highly imbalanced data classification problems are addressed using a deep learning method with the over-sampling method to handle industrial conditional monitoring and fault detection problems. Firstly, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), an adversarial learning network is adopted for generating additional data samples to balance the numbers of samples between the majority and minority classes. Then, the Fuzzy ARTMAP (FAM) model is employed for data classification. A real-world condition monitoring problem is conducted as a case study to evaluate the usefulness of WGAN-GP-FAM in practical environments. 2023-02 Thesis https://shdl.mmu.edu.my/12872/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Engineering and Technology (FET) EREP ID: 12296
institution Multimedia University
collection MMU Institutional Repository
topic Q300-390 Cybernetics
spellingShingle Q300-390 Cybernetics
Chang, Timothy Zhi Wei
Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring
description Condition monitoring is a process of assessing the health status of a system, process, or machine. Monitoring and identifying any potential fault can be conducted by leveraging measurements from the installed sensors that provide information on the state of the system. In this respect, machine learning models are useful for processing and analysing the sensor data for fault detection. However, the imbalanced nature of these sensory data can cause misleading high accuracy scores. In this research, the highly imbalanced data classification problems are addressed using a deep learning method with the over-sampling method to handle industrial conditional monitoring and fault detection problems. Firstly, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), an adversarial learning network is adopted for generating additional data samples to balance the numbers of samples between the majority and minority classes. Then, the Fuzzy ARTMAP (FAM) model is employed for data classification. A real-world condition monitoring problem is conducted as a case study to evaluate the usefulness of WGAN-GP-FAM in practical environments.
format Thesis
qualification_level Master's degree
author Chang, Timothy Zhi Wei
author_facet Chang, Timothy Zhi Wei
author_sort Chang, Timothy Zhi Wei
title Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring
title_short Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring
title_full Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring
title_fullStr Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring
title_full_unstemmed Generative Adversarial Network and Fuzzy ARTMAP for imbalanced data classification in condition monitoring
title_sort generative adversarial network and fuzzy artmap for imbalanced data classification in condition monitoring
granting_institution Multimedia University
granting_department Faculty of Engineering and Technology (FET)
publishDate 2023
_version_ 1811768013590888448