Machines

Aviation hydraulic systems operate under high pressure and large flow rates, which induce significant fluid pressure pulsations and hydraulic shocks in pipelines. These pulsations, exacerbated by complex external loads, can lead to excessive vibration stress, component damage, oil leakage, and compromised system safety. While existing methods—such as pump structure optimization, pipeline layout adjustment, and active control—can reduce pulsations to some extent, they are limited by cost, reliability, and adaptability, particularly under high-pressure and multi-excitation conditions. Passive control, using pressure pulsation damping devices, has proven to be more practical; however, conventional designs typically focus on low-load systems and have limited frequency adaptability. This paper proposes a multi-hole parallel pressure pulsation damping device that offers high vibration attenuation, broad adaptability, and easy installation. A combined simulation–experiment approach is employed to investigate its damping mechanism and performance. The results indicate that the damping device effectively reduces vibrations in the 200–500 Hz range, with minimal impact from changes in load pressure and rotational speed. Under a high pressure of 21 MPa and a speed of 1500 rpm, the maximum insertion loss can reach 15.82 dB, significantly reducing the pressure pulsation in the hydraulic pipeline.

A machine learning (ML) methodology for the robust detection and severity characterization of incipient gear faults under variable speed and load is postulated. The methodology is trained using vibration signals from a single accelerometer mounted on the gearbox, simultaneously acquired with tachometer signals at a sample of working conditions (WCs) from the range of interest. A special parametric identification procedure of gearbox dynamics that may account for the continuous range of WCs is introduced through `clouds’ of advanced stochastic data-driven Functionally Pooled models, estimated from angularly resampled vibration signals. Each cloud represents the gearbox dynamics at a specific fault severity level, while the pseudo-static effects of the WCs on the dynamics are accounted for through data pooling. Fault detection and severity characterization are achieved by testing the consistency of a vibration signal with each model cloud within a hypothesis testing framework in which the unknown load is also estimated. The methodology is assessed through 18,300 experiments on a single-stage spur gearbox including four incipient single-tooth pinion faults, 61 speeds, and four load levels. The faults produce no significant changes in the time-domain signals, while their frequency-domain effects overlap with the variations caused by the WCs, rendering the diagnosis problem highly challenging. The comparison with a state-of-the-art deep Stacked Autoencoder (SAE) demonstrates the ML method’s superior performance, achieving 95.4% and 91.6% accuracy in fault detection and characterization, respectively.

This paper studies the progressive damage process and final damage form of composite laminate aircraft radome under high-speed hail impact A simulation method based on Peridynamic bond-based theory is proposed to study the progressive damage process and final damage form of composite laminate aircraft radome under high-speed hail impact. Using the Peridynamic theory, the dynamic damage behavior of hailstone impact on a composite laminate plate is analyzed, and an impact model of hailstone impact is established to study the damage initiation, expansion, and failure behavior of the composite laminate. The dynamic mechanical constitutive and failure criteria that characterize the macromechanical behavior of both hailstone and composite laminate during impact are established. Additionally, equations describing the interaction forces between these two materials are proposed to develop a numerical simulation method for the laminate failure process. The dynamic damage evolution and failure mechanisms are subsequently investigated to provide a theoretical foundation for the optimum design of composite structures, such as aircraft radomes, subjected to hail impact. To describe the interaction force equations between two materials, a new method based on Peridynamics (PD) is proposed to establish a numerical simulation method for the damage process of laminated plates. This method provides a theoretical basis for optimizing the design of composite structures (such as aircraft radome) after being impacted by hail.