Authors - Sakshi Prakash Masand, Sadhana Shashidhar, Animesh Giri Abstract - High-stakes industries like the aviation industry demand minimal downtime requiring unified solutions that address all maintenance and troubleshooting needs. Existing solutions often function in isolation, requiring multiple systems for predictive analytics and manual-based repairs. To bridge this gap, this article presents a novel integration of machine learning based predictive maintenance and conversational AI for routine servicing and troubleshooting operations in industrial IoT systems, tailored to critical sectors such as aviation. A robust predictive maintenance model is built to predict the Remaining Useful Life (RUL) for aircraft components. Multiple traditional machine learning models like Random Forest, Support Vector Regression (SVR), XGBoost, and deep learning techniques like Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) are compared for performance and accuracy, ultimately focusing on a refined time series specific approach. When the predicted RUL of a component falls below a predefined threshold, operators are automatically alerted to schedule maintenance. For interactive support, Rasa, a customisable conversational AI framework and a fine tuned LLaMA model provide instant and enterprise specific guidance, offering step by step instructions and reducing reliance on lengthy manuals. This solution combines predictive maintenance with dynamic assistance, saving valuable time and resources for the industry.
