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Application of Over-Sampling Techniques and Fuzzy ARTMAP to Condition Monitoring of a Power Generation System

conference contribution
posted on 2023-05-29, 23:50 authored by TZW Chang, SC Tan, KS Sim, Chee Peng LimChee Peng Lim, PY Goh
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.

History

Volume

00

Pagination

233-238

Location

Putrajaya, Malaysia

Start date

2023-03-06

End date

2023-03-07

ISBN-13

9781665475013

Language

eng

Title of proceedings

2023 IEEE 3rd International Conference in Power Engineering Applications: Shaping Sustainability Through Power Engineering Innovation, ICPEA 2023

Event

2023 IEEE 3rd International Conference in Power Engineering Applications (ICPEA)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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