## 1 Introduction
Therefore, with the help of yaw angle data, it is easier to identify certain changes in the service environment, such as a vertical dive or pull-up environment. However, for service environments such as air-air, medium-air-ground, and air-ground states, one flight profile can include many climb-descent flight sessions, and the time of a single climb-descent flight is so long that the environment cannot be distinguished using coupled flight parameters. It should be noted that climb-descent flight in this article is defined as the process of anticipation motion for some other training missions (such as rapid barrel rolling, turning with a small radius, inverted flight, and cruise). Meanwhile, without the periodicity, methods such as rain flow filtering [15; 16; 17] and cycle counting [18], which are always used in the compilation of aircraft spectra, have difficulty extracting flight altitude features because the outputs of these methods are also data about cycles. Though the big data regarding altitude have non-ergodic properties, methods such as time-delay correlation [19] and empirical mode decomposition [20] used in the statistical field for non-ergodic data are highly dependent on frequency information and are unsuitable for extracting altitude features. The time-delay method is essentially a time domain form corresponding to the frequency domain method for data with periodicity, and empirical mode decomposition is also a theory that decomposes signals from low frequency to high frequency in turn. Due to the uncertainty of altitude time history, the methods that determine the distance between two maneuvering profiles, such as principal component analysis [4], neural networks [21], or other types of machine learning algorithms [22], cannot be directly applied to cluster altitude data. These methods often classify signals based on similar matrices, which means that the objects represented by the analyzed data have some similar implicit features. However, as the altitude time histories themselves do not have these features, we need to identify an algorithm that is able to look for parameters that can represent similar properties from the altitude data. The abovementioned issues make it difficult to solve the problem of flight service environment classification relying on altitude features, so the proportions of the flight time of a certain service environment cannot be determined. Therefore, even if the time history of the main bearing load under a certain service environment could be worked out [23], a corresponding flight service environment spectrum cannot be compiled, making it impossible to evaluate the life of the components in aeroengines. In this article, through filtering for trends and removing redundancies from peak-valley (PV) values, a flight altitude feature extraction method related to the Frequency of Climb-Descent Flight (FCDF) is presented. With the FCDF and the maximum flight altitude as input parameters, a clustering algorithm for the aircraft service environment is established based on fuzzy theories, and then the compilation flowchart of the service environment spectrum, which is related to flight altitude, is given. Through some practical examples, the effectiveness of the altitude feature extraction algorithm is verified, and the specific implementation procedure of the environment spectrum compilation is explained in detail.