LabVIEW-based fire extinguisher model based on acoustic airflow vibrations

Abstract

In recent years, soundwave-based fire extinguishing systems have emerged as a promising avenue for fire safety measures. Despite this potential, the challenge is to determine the exact operating parameters for efficient performance. To address this gap, we present an artificial intelligence (AI)-enhanced decision support model that aims to improve the effectiveness of soundwave-based fire suppression systems. Our model uses advanced machine learning methods, including artificial neural networks, support vector machines (SVM) and logistic regression, to classify the extinguishing and non-extinguishing states of a flame. The classification is influenced by several input parameters, including the type of fuel, the size of the flame, the decibel level, the frequency, the airflow, and the distance to the flame. Our AI model was developed and implemented in LabVIEW for practical use. The performance of these machine learning models was thoroughly evaluated using key performance metrics: Accuracy, Precision, Recognition and F1 Score. The results show a superior classification accuracy of 90.893% for the artificial neural network model, closely followed by the logistic regression and SVM models with 86.836% and 86.728% accuracy, respectively. With this study, we highlight the potential of AI in optimizing acoustic fire suppression systems and offer valuable insights for future development and implementation. These insights could lead to a more efficient and effective use of acoustic fire extinguishing systems, potentially revolutionizing the practice of fire safety management