Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate.

During the ascent phase of a missile, a challenging problem occurs that blocks the construction of a high-precision attitude control scheme, which directly affects accurate modeling including disturbances: non-linearities of an actuator, rapidly time-varying parameters, un-modeled dynamics, etc. In order to improve the control performance, an active disturbance rejection control (ADRC) scheme, considering non-linear dynamics of the actuator and wind disturbance during the ascent phase, is proposed in this paper. An expand state observer (ESO) is planned to estimate and compensate the actuator’s non-linear dynamics, flight model uncertainties, and wind disturbance. Therefore, the complex non-linear time-varying control problem is simplified into a linear time-invariant control problem. The pitch attitude control system is controlled by the cascade method and ADRC controllers are designed for actuator close loop and attitude control loop, respectively. The simulation results show that ADRC has strong robustness under different dead-zones and external disturbances of the actuator. On the other hand, ADRC can effectively suppress the external atmospheric disturbance. Compared with the traditional gain-scheduling control scheme, the ADRC scheme can significantly reduce the overloading of the system and shows remarkable performance for tracking as well as wind resistance.

In this paper, based on the advantages of Newtonian mechanics method, mixed coordinate method, and analytical mechanics method, the dynamic model of rigid-aeroelasticity-sloshing coupling is established for longitudinal vibration and liquid fuel sloshing, as well as the coupling between elastic deformation and liquid sloshing and their influence on the liquid rocket motion are analyzed. Considering the coupling effect between aeroelasticity and liquid sloshing, a new flutter analysis model is proposed, and the corresponding flutter analysis is carried out. First, the simplified model of the body and the liquid of the tank are established. By using Newtonian mechanics and analytical mechanics method, the nonlinear structural dynamic model considering the 3-D vibration and liquid sloshing is obtained. Second, influence of rigid-aeroelasticity, rigid-sloshing, aeroelasticity-sloshing, and rigid-aeroelasticity-sloshing coupling is simulated and analyzed. Finally, a new simplified model for the analysis of the flutter of the body is built by taking into account the impact of the liquid fuel sloshing. The results demonstrate that the elastic deformation and liquid sloshing change the motion characteristics of the rocket, and there is a strong coupling between the aeroelasticity and sloshing. Meanwhile, liquid sloshing has different effects on the flutter in all directions.

With the application of advanced composite materials in High-Aspect-Ratio wings (HARW), the randomness of structural parameters, such as elastic modulus and Poisson's ratio, is enhanced. Hence, in order to explore the whole picture of aeroelastic problems, it is of great significance to study the role of random structural parameters in aeroelastic problems. In this paper, the dynamic response of flexible HARW considering random structural parameters is analyzed. An aeroelastic model of a one-dimensional cantilevered Euler–Bernoulli beam considering aerodynamic forces acting on the wing is established based on Hamilton's principle. Adopted the idea of simplifying calculation, the effect of random structural parameters is analyzed. Then, considering the elastic modulus and torsional stiffness as continuously one-dimensional random field functions, and discretized by local method. The first and second order recursive stochastic nonlinear finite element equations of wing are derived by using perturbation method. Based on it, statistical expression of aeroelastic effects of the wing is derived. Monte Carlo method is adopted to verify the effectiveness of the method. Numerical simulations indicate that the method proposed can well mirror the statistical characteristics of aeroelastic response.

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.