METHODOLOGICAL REVIEW OF HOSE TESTING MACHINE: COMPARATIVE STUDY OF DIFFERENT TESTING METHODS

The efficient management of delivery hoses is important in various firefighting applications, particularly in water based systems where leakage can lead to significant operational and safety challenges. This review paper examines recent advancements in delivery hose pipe testing technologies, focusing on methodologies in different Indian and international standards for detecting and managing leaks. The research emphasizes the creation of an AI-driven leak detection system that employs acoustic listening techniques, which, although effective, tend to be labor-intensive and susceptible to human mistakes. o make leak detection better, researchers have tried using machine learning techniques like Deep Neural Networks, Convolutional Neural Networks, and Support Vector Machines to make it more accurate and efficient.. The results indicate that Thermal Imagining outperforms other models, achieving over good result in field trials, thus providing a reliable tool for operators. The paper examines the role of Internet of Things (IoT) technologies in hose integrity monitoring while addressing potential cybersecurity risks. By evaluating statistical and AI-driven failure prediction models, this study provides critical insights for selecting optimal testing methodologies for fire hose evaluation. The conclusions emphasize the significance of advanced testing technologies in enhancing the reliability and safety of firefighting operations.