## 6 Conclusions  
This study highlights the problems related to flight service environment spectrum compilation. As an important indicator of aircraft maneuverability, altitude is currently always used to compile service environment spectra. Therefore, this study researched compilation methods that utilize altitude data. A method of extracting the FCDF from flight altitude data was presented first, and then, taking the maximum flight altitude and the FCDF as fuzzy clustering input parameters, a service environment spectrum compilation method related to the FCDF and maximum flight altitude was presented. The effectiveness of this method was verified through flight examples, and the implementation process of the service environment spectrum compilation approach was also discussed. The results showed that there is no positively correlated relationship between the FCDF and maximum altitude; there are more training sessions for climb-descent actions at mid- to low-altitude and high-altitude flights, while low-altitude and middle-altitude not only account for a lower proportion of flight time but also have a lower number of training sessions for climb-descent actions during single takeoff-to-landing flights. Although the FCDF was extracted, we ignored a number of features that were considered to be more easily identified with the help of turn data, Mach data, or other data from the aircraft. One can also study the feature extraction methods for other flight parameters and cluster the service environment using those. More clustering parameters can improve the robustness of the method, and one can further study the service environment spectrum \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cluster Variables**} & \multicolumn{3}{c}{\((\boldsymbol{hp_{\max}}\),\(\boldsymbol{n_{t}}\))} & \multirow{2}{*}{**Mach (\%)**} & \multirow{2}{*}{**Turning Number**} & \multirow{2}{*}{**Time (\%)**} \\ \cline{2-2} \cline{5-6}  & **Maximum Altitude (\%)** & & & & \\ \hline
1 & 18.93 & 1.65 & 38.46 & 5 & 20.85 \\
2 & 35.59 & 2.78 & 49.75 & 14 & 29.80 \\
3 & 57.24 & 1.83 & 71.00 & 27 & — — \\
4 & 78.14 & 3.69 & — — & — & — \\ \hline \end{tabular}
\end{table} 
Table 2: Cluster centers of multiple parameters. Figure 10: Service environment spectrum obtained from multiple parameters of aircraft. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Class**} \\ \hline
1 & (2, 1, 3, 1) & 6 & (3, 2, 2, 2) \\
2 & (4, 2, 2, 2) & 7 & (4, 2, 1, 2) \\
3 & (2, 1, 2, 1) & 8 & (1, 1, 1, 1) \\
4 & (4, 2, 2, 1) & 9 & (3, 2, 1, 1) \\
5 & (2, 1, 1, 2) & 10 & (3, 1, 1, 2) \\ \hline \end{tabular}
\end{table} 
Table 3: The \(10\) classes with the maximum time proportion of the 72 service environments. compilation method using multiple flight parameters. Meanwhile, we only established a method that can compile the flight environment spectrum in more detail. There are many parameters that need to be determined using practical experience, and we did not identify any algorithms that can be set adaptively, meaning that the robustness of the method used in this article needs to be further improved. Furthermore, the cluster method established in this article can be extended to multiple flight parameters, the compilation of flight service environment spectra, and the classification of flight missions. When compiling the service spectrum, we can use the flight parameters of one flight profile in each cluster as the inputs of the main bearing model: the loads of the main bearing model for each cluster can be calculated and the life can be evaluated using the time proportion of the environment spectrum. These methods may not only be applied to the main bearings of aeroengines but also to other components of aircraft or engines. Methodology, M.C. ; investigation, M.C. ; resources, X.W., H.W. and G.L. ; writing--original draft preparation, M.C. ; writing--review and editing, M.C. ; funding acquisition, M.C.