###### Abstract  
Complex aviation systems' integrity deteriorates over time due to operational factors; hence, the ability to forecast component remaining useful life (RUL) is vital to their optimal operation. Data-driven prognostic models are essential for system RUL prediction. These models benefit run-to-failure datasets the most. Thus, significant factors that could affect systematic integrity must be examined to quantify the operational component of RUL. To expand predictive approaches, the authors of this research developed a novel method for calculating the RUL of a group of aircraft engines using the N-CMAPSS dataset, which provides simulated degradation trajectories under real flight conditions. They offered bootstrap trees and hyperbolic tangent NtanH(3)Boost(20) neural networks as prognostic alternatives. The hyperbolic tangent boosted neural network uses damage propagation modelling based on earlier research and adds two accuracy levels. The suggested neural network architecture activates with the hyperbolic tangent function. This extension links the deterioration process to its operating history, improving degradation modelling. During validation, models accurately predicted observed flight cycles with 95-97% accuracy. We can use this work to combine prognostic approaches to extend the lifespan of critical aircraft systems and assist maintenance approaches in reducing operational and environmental hazards, all while maintaining normal operation. The proposed methodology yields promising results, making it suitable for adoption due to its relevance to prognostic difficulties. W +
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its present condition and prior performance [3; 4]. Prognostic maintenance methodologies have the potential to reduce costs and improve dependability. Numerous methodologies have been suggested to facilitate the anticipation of RUL and prognosis. However, traditional limitations such as data accessibility, system intricacy, implementation prerequisites, and the presence of monitoring equipment hinge on the selection of an appropriate strategy. There is a lack of consensus regarding a universally accepted set of metrics suitable for prognostics. The process of selecting appropriate metrics for prognostics is inherently uncertain for both researchers and practitioners in the field of condition-based monitoring (CBM). Therefore, when utilising decisional metrics to determine preventive actions, prognostic tools must consider this inherent uncertainty. There are a multitude of potential methodologies for data analysis in the context of predictive maintenance. The most widely favoured options are enumerated as follows. The physics-based methodology necessitates a precise physical representation of system behaviour, encompassing both normal and defective states [5]. By comparing the data obtained from sensors with the model's predictions, one can infer the health of the system [6]. Physical techniques encompass the utilisation of physics-of-failure models. One approach to failure mode analysis entails integrating experiments, observation, geometry, and condition monitoring data in order to assess the extent of damage caused by a specific failure mechanism. Data-driven methods use historical data to make predictions about a system's future state or identify similar trends in the past in order to estimate RUL [7]. The aforementioned factors encompass statistical methodologies, reliability functions, and artificial intelligence (AI) techniques [8]. The following is a list of the subsequent prognostic approaches based on the data:  
* The various techniques employed in the field of artificial intelligence include neural networks, fuzzy logic, decision trees, support vector machines (SVMs), anomaly detection algorithms, reinforcement learning, classification, clustering, and Bayesian methods. * Academic research and practical applications commonly employ various conventional numerical techniques, such as wavelets, Kalman filters, particle filters, regression, and statistical methods.