UPM Institutional Repository

Development of fault detection and diagnosis for reactor cooling system by using artificial intelligent techniques


Citation

Abdul Rahman, R. Z. and Syafiee Anuar, M. A. and Mohd Aziz, M. A. F. and Che Soh, A. and Mohd Noor, S. B. and Abdul Karim, J. (2023) Development of fault detection and diagnosis for reactor cooling system by using artificial intelligent techniques. International Advance Journal of Engineering Research, 6 (12). pp. 5-10. ISSN 2360-819X

Abstract

Reactor Cooling System (RCS) equipped with a safety system that will trigger when the reading from the sensor exceeds the threshold of normal operation. Fault Detection and Diagnosis (FDD) system is one of the safety measures that have been in ensuring the safety of the reactor. Act in giving immediate response when the faults occur and have the capability to identify the faults location. This allows the operator to react swift and according if any disturbance were to happen. In realizing this, a model-based FDD system, a system modelling and fault diagnosis algorithm need to be studied. For this study, two artificial intelligence techniques have been applied which are Adaptive Neuro Fuzzy Inference System (ANFIS) for system modelling and Artificial Neural Network (ANN) to diagnose the fault on a reactor cooling system. The ability of neural networks to learn from experience or previous data has demonstrated a significant improvement in fault detection efficiency. Additionally, a history-based strategy that is based on historical data has been shown to improve the accuracy of fault identification. As a result, complete FDD systems that successfully detect and classify 8 fault classes with performance of 96 accuracy have been developed.


Download File

Full text not available from this repository.
Official URL or Download Paper: https://www.iajer.com/volume-06-issue-12/

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Publisher: International Advance Journal of Engineering Research
Keywords: Fault Detection; ANFIS modelling; ANN classification
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 15 Oct 2024 06:56
Last Modified: 15 Oct 2024 06:56
URI: http://psasir.upm.edu.my/id/eprint/107309
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item