Abstract
A significant obstacle to creating efficient machine health monitoring systems is estimating performance degradation in dynamic systems, like aero plane engines. In exceedingly complex systems with many components, states, and parameters, conventional model-based and data-driven methods fall short of producing satisfactory results. While traditional methods had several drawbacks, deep learning has emerged as a viable computational tool for dynamic system prediction. In order to track system deterioration and estimate the EGT, a novel technique based on the Long Short-Term Memory (LSTM) network, (an architecture created to find the hidden patterns hidden in time series data) is provided in this research. The health monitoring information of aircraft turbofan engines is used to assess the effectiveness of the proposed strategy. As a result of this network's ability to recognize the input data as a real-time series, the output in the following step can be predicted. Results of the suggested study show a significant ability to anticipate the output in the following time step. Additionally, the proposed model has a shorter learning curve and is more accurate.