Authors: Deepa Barethiya, Kshitij Moon, Dipak Meshram
Abstract: Accurate Remaining Useful Life (RUL) prediction for turbofan engines is critical for implementing effective condition-based maintenance strategies, enhancing operational safety, and reducing maintenance costs. Traditional predictive maintenance approaches often struggle with the non-linear, time-dependent characteristics of engine degradation. This paper presents a data-driven prognostic model utilizing a Long Short-Term Memory (LSTM) neural network to predict the RUL of turbofan engines based on sensor-derived operational data. The model is trained and validated on the NASA C-MAPSS dataset, which contains run-to-failure data for multiple turbofan engines. The proposed methodology involves preprocessing raw sensor data, creating sequential inputs using a sliding window approach, and training a two-layer LSTM architecture designed to learn complex temporal degradation patterns. Model performance is evaluated using standard regression metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score. The resulting model demonstrates robust predictive capabilities and is deployed in a Flask-based web application, offering a practical tool for real-world CBM systems and highlighting the efficacy of deep learning for industrial prognostics.
