Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection.

Aiming at the fault detection problem of aero-engine actuators, a fault diagnosis method for aero-engine actuators based on adaptive RBF network observer is proposed. Firstly, the system model is established for the faulty actuator, and then the observer-based actuator fault diagnosis principle is established. Secondly, for the problem that the traditional observer is not sensitive to the gradual fault, the nonlinear approximation characteristics of the RBF network are used. The adaptive fault observer design method based on RBF network is applied. At the same time, the sufficient conditions for the convergence of the observer error system are given by using the Lyapunov stability analysis method. Finally, the simulation experiments of different types of actuators for a turbofan engine are carried out. The results show that the adaptive RBF observer designed in this paper is more effective in detecting faults and has certain fault estimation ability.

After an aero-engine shuts down, the high temperature within the core flow path prevents conventional borescope probes from performing immediate internal inspections due to their limited thermal resistance, thereby constraining rapid turnaround capabilities for aircraft. To address this challenge, this study proposes an active cooling strategy using coolant flow to keep the probe within a safe temperature range. Three cooling structures incorporating pressure-drop modules—annular, annular-slit, and round-hole configurations—were designed and numerically investigated to assess the effects of geometric parameters and coolant properties (temperature, pressure, nitrogen mixing ratio) on cooling performance. The results demonstrate that the round-hole structure with a 1.0 mm diameter achieves optimal cooling, maintaining an average probe mirror temperature of 286.2 K under coolant conditions of 285 K and 0.5 MPa. Cooling efficiency exhibits a strong linear negative correlation with coolant temperature, while its relationship with pressure is highly structure-dependent. Nitrogen doping significantly improves the heat transfer capacity of the coolant. The implemented three-stage pressure-drop module performs consistently, with the pressure loss per stage determined solely by the inlet pressure. This study provides valuable insights and a theoretical foundation for the design of high-temperature-resistant borescope equipment capable of operating in the harsh environments of aero-engines.

Aircraft engine endoscope probes often face difficulties in effectively detecting internal structures in high-temperature environments. In order to improve the thermal protection characteristics of endoscope probes, this paper designs a probe-cooling structure with a circular ring pressure drop structure and calculates and analyzes the cooling effect of the probe under different gas cooling conditions. Study the influence of size parameters and cooling medium properties of the structure on the cooling characteristics of the probe, analyze the temperature distribution of the probe mirror, cooling efficiency distribution, and cold flow outlet flow distribution. The results show that the larger the outlet width of the annular cooling structure, the better the cooling effect, and the optimal cooling effect structure is 0.7 mm; the larger the opening angle, the lower and then the higher the temperature of the endoscope probe surface, and the best cooling effect occurs when the optimal angle is 40°; the larger the proportion of mixed liquid nitrogen, the lower the temperature of the probe mirror surface. A 5% proportion of mixed liquid nitrogen can reduce the temperature of the probe mirror surface by about 11 K.

This study proposes a genetic algorithm-enhanced dhole optimization algorithm coupled with a long short-term memory network (GA-DOA-LSTM) for dynamic identification of a high bypass ratio separate-flow turbofan engine during acceleration. A component-level JT9D model was implemented in T-MATS, and a dataset was generated at 54 flight operating points under step, ramp, and parabolic throttle excitations. Low- and high-pressure spool speeds, high-pressure compressor outlet total pressure, combustor exit temperature, and fuel flow were used as inputs, while low-pressure turbine exit temperature (T5) and thrust were predicted. GA-DOA combines dual-population coevolution, a global optimum pool, periodic elite exchange, greedy selection, and a final DOA refinement to jointly optimize the LSTM architecture and output loss weights. Compared with LSTM, PSO-LSTM, SSA-LSTM, DOA-LSTM, and GA-LSTM, the proposed model achieved RMSE, MAE, and MAPE values of 1.6121 K, 1.0136 K, and 0.1512% for T5 and 2430.10 N, 1832.24 N, and 1.2576% for thrust, respectively. The model maintained consistent transient tracking under different altitudes, Mach numbers, and throttle profiles, demonstrating its potential for aero-engine performance assessment, health monitoring, and control-oriented modeling. The main limitation is that the present validation remains simulation-based and engine-specific; experimental and cross-engine validation will therefore be required before practical deployment.