Implementation of a Multiple Sensory System for the Detection of Fluid Losses in Ducts through Adaptive Neuro-Fuzzy Inference

Implementation of a Multiple Sensory System for the Detection of Fluid Losses in Ducts through Adaptive Neuro-Fuzzy Inference
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© 2024 by IJETT Journal
Volume-72 Issue-11
Year of Publication : 2024
Author : Marco Carbajal-López, Anthony Valdivia-Diaz, Jose Briones-Zuñiga, Carlos Sotomayor-Beltran
DOI : 10.14445/22315381/IJETT-V72I11P128

How to Cite?
Marco Carbajal-López, Anthony Valdivia-Diaz, Jose Briones-Zuñiga, Carlos Sotomayor-Beltran, "Implementation of a Multiple Sensory System for the Detection of Fluid Losses in Ducts through Adaptive Neuro-Fuzzy Inference," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 279-293, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P128

Abstract
This work presents a sensor-based fluid pipeline leak detection system using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The implemented model consists of multiple flow sensors and pressure differentials, processed through a hybrid Neural Networks and Fuzzy Logic hybrid system. The network comprises five layers, with layer 1 being in charge of performing the membership function and layer 5 being part of the calculation of the exit rule. The system aims to detect and prevent fluid leakage in fluid passages by identifying changes in fluid flow and pressure differential. The results demonstrate that the ANFIS system can accurately detect leaks in the ducts, reaching 93.24 % of accuracy, indicating the percentage of correct predictions in the training set. Additionally, a validation set not part of the training was used for the model's generalisation ability. This data set allowed us to measure patterns and characteristics of the model with new and previously unseen data.

Keywords
Leak detection, Pressure, ANFIS, Neural network, Python, TensorFlow.

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