• Rama Mishra
• E.vijay Kumar

To achieve higher levels of performance, safety critical systems are becoming larger and more complex. Therefore, modeling and evaluating these systems can be difficult and error-prone. Among existing security models, fault tree analysis (FTA) is a well-known method and its graphical structure is easy to understand. This research presents a new approach that uses machine learning (ML) and Realtime data to understand natural behavior. Then, if there is a problem with the reference to the behavior model, the method tries to find the description of the error in the error tree and then shares the information with the person. answers the phone. If the fault tree cannot explain the situation, there are many different suggestions, including correcting the fault tree, depending on the nature of the situation. Decision trees are used for this purpose. The effectiveness of the proposed method is demonstrated with a hypothetical example of a Mining Industry

Fault tree, reliability, safety modelling, model repair, machine learning, artificial intelligence.

"Industrial Safety with Artificial Intelligence and Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.g661-g671, March-2024, Available :http://www.jetir.org/papers/JETIR2403689.pdf

"Industrial Safety with Artificial Intelligence and Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppg661-g671, March-2024, Available at : http://www.jetir.org/papers/JETIR2403689.pdf