###### Abstract  
Compared to maximum state parameters, such as maximum altitude and Mach, the number of hovers and S turns can be used as process parameters representing the complexity of military aircraft maneuvers when classifying big flight mission data to compile flight load spectra for structures. This study developed intelligent statistical algorithms based on yaw angle data from flight parameters such as the number of hovers and S turns. Using the median-crossing de-redundant function of Phase-Sensitive Detection (PSD) and analyzing the characteristics of 360\({}^{\circ}\) hovering flight parameters, a statistical algorithm for the number of hovers during a flight profile is presented. Using the split-half function of PSD, a triangle layering algorithm based on the yaw angle signal was developed to count the number of S turns during a flight profile, where the signal of each sublayer is segmented into median-crossing intervals to eliminate the redundant median-crossing marks from the previous layer. Compared with artificial means, the statistical results of the flight example showed that the developed intelligent algorithms are effective. aircraft; intelligence; load spectrum; phase-sensitive detection; turn +
Footnote †: journal: Applied sciences  
The actual flight mission of military aircraft has very serious subject frequency mixing, and there are many subjects for the same engine models under different service environments; some subjects even include many subcategories generated at different flight altitudes and speeds so that the actual subjects cannot compile full-scale spectra for the rotor, and the dynamic simulations require further classification to flight subjects. Hovers and S turns that reflect the directional changes in the flight process are closely related to the complexity of maneuvers, and many violent maneuvering actions need hovers or S turns as auxiliary movements, such as fancy somersaults, barrel rolls, etc. The number of hovers and S turns--along with the maximum altitude, maximum Mach, and flight time--are the feature parameters of flight mission classification for big flight data of the same engine model; in this study, we focus on introducing the flight state denoted by the number of hovers, and S turns into the compilation framework of load spectrum, similar to the current maximum flight states. However, studies using flight turn counting or detailed maneuvering actions to classify flight missions in big flight data are difficult to find; therefore, this study investigated a statistical method to analyze the number of hovers, and S turns by extracting the yaw angle features to create a pioneering exploration in this field. Hovers and S turns during aircraft cruising may not be ideal actions due to weather, geographical conditions, flight operations, etc. ; an aircraft often experiences occasional changes in direction during hover or S turn actions, and yaw angle data during these maneuvers often appear as violent jumps and weak fluctuations. This makes statistical analysis of the number of hovers, and S turns difficult. The actual flight parameter data have many weak fluctuations, so the peak-valley value method, which is suitable for calculating the number of inflection points, cannot be used for counting the number of hovers and S turns. Due to the instability of frequency, it is challenging for methods such as rain flow filtering [11] and cyclic counting, which are often used in the compilation of aircraft spectra, to accurately calculate the number of hovers and S turns in a flight profile, while other methods such as time-delay correlation [12] and empirical mode decomposition [13], used in the statistical analysis field of non-ergodic data, are highly dependent on frequency information and are not suitable for calculating the number of hovers and S turns.