Assessing Gas Leakage Detection Performance Using Machine Learning with Different Modalities

Artificial intelligence technologies are reviving the entire proactive repair system for leakage issues in industries with a real-time strategy for the current era of industry 4.0. In the present work, Gas sensor and an infrared thermography is used in this work to analyze the effectiveness of finding gas leaks. The effectiveness of the proposed work is to extract the statistical features from gas sensor and infrared thermography to identify gas leaks. Moreover, different machine learning methods are renowned for performing exceptionally well in classification tasks, to obtain accurate categorization. To properly identify and classify gas leakages, four separate categories have been established on the most popular online dataset. On a different gas leakage dataset, different tests have been conducted to verify the methodology and evaluated our strategy against other machine learning techniques that are frequently employed in gas leakage detection. The results show how well the proposed method works for precisely identifying and categorizing gas leaks. The comparison analysis demonstrates the different machine learning methods and their superiority in terms of efficiency and classification precision.