## 4 Discussion and Results  
The methodology employed in generating the dataset involves the identification and analysis of the failure modes associated with the sub-components of the main engine. Figure 4 depicts a comprehensive methodological profiler for predicting the RUL of engines,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tree** & **Splits** & **Rank** & **OOB Loss** & \ \begin{tabular}{c} **OOB** \\ **Loss/N** \\ \end{tabular}  & **RSquare** & **IB SSE** & **IB SSE/N** & **OOB N** & **OOB SSE** & \begin{tabular}{c} **OOB** \\ **SSE/N** \\ \end{tabular}  \\ \hline
1 & 1069 & 19 & 348,428.61 & 0.17987  
+++ ==WARNING: Truncated because of repetitions==
3 & 0.9996 & 588,249.73 & 0.1117613 & 1,937,081 & 345,175.5 & 0.1781936 \\  
++  
which clearly showcases the positive, neural, and negative properties and profilers that influence the entire RUL prediction process. The components that exhibit a consistent decrease are the fan, low pressure compressor (LPC), high pressure compressor (HPC), low pressure turbine (LPT), and high-pressure turbine (HPT). The simulation of deterioration effects involves the modification of the flow capacity and efficiency of the engine sub-components, which are denoted by the engine health parameters \(\theta\). The given quantity of hyperbolic tangent boosted nodes is three, while a sequence of models that are additive in nature and scaled by the boosting learning rate is set to 0.1. The penalty method is mathematically defined as the square of a certain value, where the number of iterations is fixed at one. Figure 5 presents visual depictions of the highest rated response contours for pairs of variables, obtained by extracting notable positive and negative features through profiler manipulation. The interactive contour profiling capability is used to visually improve response surfaces. Every contour is accompanied by a dashed line that shows the direction of higher response values, giving a distinct indicator of orientation. Figure 4: Depiction of the visual representation of prediction profiler traces that involve numerous inputs. The prediction profiler displays profile traces for every \(x\) variable. A profile trace refers to the anticipated outcome when a single variable is altered while keeping all other variables at their present levels. The prediction profiler recalculates the profiles and projected reactions instantaneously when you modify the value of an \(x\) variable. The vertical dashed line for each \(x\) variable represents its current value or configuration. The value shown above each factor name is the current value of the corresponding \(x\) variable. The horizontal dashed line represents the present forecasted value of each \(y\) variable based on the current values of the \(x\) variables. To fully understand how the \(x\) factors are affecting the expected values, some variables show profiles with positive slopes, while others display negative slopes. For instance, the variable T40 has a favourable upward trend. This implies a clear relationship between the total temperature at the burner output T40 and the anticipated median value. Specifically, as the total temperature rises, the predicted median value is \(\pm\) 0.001. Figure 5: Representation of the most highly rated extracted features used for predicting RUL. (**a**) Interrelated profiler relationship between ratio of fuel flow to Ps30 (phi) and total temperature at burner outlet (T40); (**b**) Interrelated profiler relationship between flow out of LPT (W50) and total temperature at HPT outlet (T48); (**c**) Interrelated profiler relationship between flow out of LPC (W22) and static pressure at HPC outlet (Ps30); (**d**) Interrelated profiler relationship between W50 and T40. value similarly increases. T48 is the total temperature at the outlet of the HPT. It has a negative slope, indicating that as the temperature rises, the median value declines. The fitting procedure of the neural network incorporates a validation technique.