Citation

Chang, Timothy Zhi Wei and Tan, Shing Chiang and Sim, Kok Swee and Lim, Chee Peng and Goh, Pey Yun (2023) Application of Over-Sampling Techniques and Fuzzy ARTMAP to Condition Monitoring of a Power Generation System. In: 2023 IEEE 3rd International Conference in Power Engineering Applications (ICPEA), 6-7 Mar 2023, Putrajaya, Malaysia.

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Abstract

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 analyzing the sensor data for fault detection. However, the imbalanced nature of these sensory data can cause misleading high accuracy scores. In this study, we employ an over-sampling method to tackle the imbalanced class problem. Specifically, both Synthetic Minority Over-sampling Technique (SMOTE) and Gaussian SMOTE are used to generate minority class samples. The balanced data set is used by the Fuzzy ARTMAP (FAM) model for fault classification. The effectiveness of the developed method is evaluated using a real-world circulating water system in a power generation plant. The results indicate that both SMOTE variants can improve the performance of FAM in detecting faults corresponding to operating conditions of the circulating water system for efficient power generation.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Imbalanced data classification, power systems, condition monitoring, SMOTE, Fuzzy ARTMAP
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Jun 2023 01:44
Last Modified:	02 Jun 2023 01:44
URII:	http://shdl.mmu.edu.my/id/eprint/11454

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