航空航天, 本领域期刊类期刊, -365足球

journal of guidance, control, and dynamics, ahead of print.

journal of guidance, control, and dynamics, ahead of print.

journal of guidance, control, and dynamics, ahead of print.

journal of guidance, control, and dynamics, ahead of print.

journal of guidance, control, and dynamics, ahead of print.

the finite element model (fem) plays an important role in model-driven response reconstruction of mechanical structure. unfortunately, modelling difficulty of certain complex mechanical substructures could lead to the unavailability of precise modelling, thus impairing the reliability of the full fem. this paper presents a measurement-based adaptive framework for substructure identification to address

monitoring rotating blade vibration is critical for turbomachinery safety, and blade tip timing (btt) is one of the key non-contact measurement techniques. conventional btt methods, however, face significant limitations in accurately identifying complex multi-frequency vibrations. these methods frequently depend on sparse time-of-arrival (toa) data, resulting in undersampling of the vibration signal

the identification and analysis of nonlinear dynamics in structural systems remain critical challenges in engineering, particularly when subtle vibrational phenomena are involved. phase-based motion magnification (pbmm) has emerged as a powerful non-contact technique for visualizing fine-scale motions in complex systems, yet its application to nonlinear structural behavior remains underexplored. this

bearing system is critical components in rotating machinery, whose health directly impacts operational safety. to address faults arising from bearing wear—and to overcome the limitations of conventional deep-learning methods that struggle to exploit both time- and frequency-domain information simultaneously—we propose fd-mvllm, a novel reprogramming framework that combines a large language model (llm)

for various semi-active rotor dry friction dampers (sardfd), how to effectively suppress rotor unbalance responses with minimum normal forces can be formulated as an optimal control problem. classical optimal controllers were mostly developed in the time domain, which makes them unsuitable for the control of sardfds due to the high rotational speeds of rotor systems. in this paper, an offline deep

large unmanned ocean platforms have extensive applications in military reconnaissance, maritime rescue, and resource exploration. the electromechanical system, as the core component of these platforms, faces numerous challenges due to harsh sea conditions, spatial constraints, and multiple monitoring points. these challenges include frequent component failures, complex multi-source data coupling, unpredictable

for degradation prediction of high-reliability equipment, accelerated degradation test data is often used to extrapolate the degradation state under normal stress levels. previous studies typically model degradation using memoryless markovian processes. however, due to the inherent degradation mechanisms and external stress influences, the degradation process often exhibits varying levels of memory

quadrotor transportation system face underactuation challenges compounded by double-pendulum dynamics and unknown payload masses. to tackle these issues, this paper develops a novel energy-coupling-based adaptive controller to address quadrotor transportation system with double-pendulum swing effects and unknown payload mass. first, a generalized payload signal is introduced to strengthen state coupling

resonance demodulation has been extensively employed for fault diagnosis in rolling element bearings due to its effectiveness in extracting fault features. achieving high diagnostic accuracy largely depends on a precise identification of the optimal resonant frequency band for demodulation. however, accuracy and robustness of existing methods still require further improvement. to address this issue

two battery pack casings with pack total energy of 12.432 kwh are 3d-printed using aluminum alloy (alsi10mg) and carbon fiber-reinforced composite filament based on polyamide 6, 12 (pa12 cf ). the vibration and shock attenuation capabilities of the three mechanical metamaterials (auxetic, lattice and x-shaped structures) applied to battery pack casing’s walls are studied. lattice structure shows better

journal of guidance, control, and dynamics, ahead of print.

we present a data-driven approach for modeling complex components in mechatronic systems using a physics-inspired neural network structure. this structure reflects the principles of classical first-principle modeling, particularly the separation of time and state-space variables. gated recurrent units (grus) approximate the system’s ordinary differential equations (odes), while a deep feedforward network

road roughness plays a significant role in vehicle vibrations, which directly affect passenger ride comfort. however, many existing estimation methods for road roughness rely on simplified linear suspension models and assume that suspension parameters remain constant, which significantly restricts their practical applicability. to overcome these limitations, this paper introduces a novel augmented

the elastic ring squeeze film damper (ersfd) is a compact damping structure designed to minimize rotor vibrations in high-speed rotating machinery. comprising bulge contact interfaces and oil-filled chambers, the ersfd exhibits complex dynamic behavior, making it difficult to model and analyze due to the interaction and mutual dependencies between contact nonlinearity and the constantly varying oil

particle filter (pf) integrated with structural health monitoring (shm) has received increasing attention for evaluating structural remaining useful life, due to its advantages of fusing online shm observation to correct results and parameters of the physical damage evolution model. to apply this technique for the situation under complex service conditions, a sophisticated damage evolution model is

the reasonable design of chaotic oscillators is related to the performance of underwater signal detection. this study proposes a new grid-scroll memristive chaotic oscillator with infinite equilibrium points, and applies it to underwater signal detection. first, based on a cubic nonlinear memristor and piecewise linear functions, a novel grid-scroll memristive chaotic oscillator is designed. the oscillator

real-time monitoring of porosity defect is crucial for quality assurance during aluminum (al) alloys laser welding. optical coherence tomography (oct) offers precise keyhole depth measurement and can be utilized for porosity monitoring. this paper proposes a laser welding porosity defect monitoring method using oct. a laser welding monitoring platform based on oct is developed to measure the keyhole

train collisions can cause serious casualties and property damage, and collision testing is a key means of evaluating the crashworthiness of trains. however, due to the complexity of the test environment, it is extremely challenging to directly measure the collision force. to this end, this paper proposes a collision load identification method that combines deep learning and optimization iteration

fault detection for wind turbine gearboxes is the pivot to ensure the sustainable and stable development of wind power. the planetary ring gear is the core component in this type of equipment. however, the challenge of ring gear fault detection is the coupling of pass-through effect and failure impulses with the same period in the time and frequency domains. traditional diagnostic methods lack the

this study examines flow and heat transfer characteristics in rotating double-wall cooling channels with side outflow, focusing on the pin-fin geometries (square, circular, rhombic) within the impingement cavity. steady-state liquid crystal measurements and steady-state rans simulations were conducted at a fixed inlet reynolds number (re = 25,000, rej = 10,150), with inlet rotation number (ro) ranges

deep anomaly detection (ad) models have shown powerful capacity in exploring abnormal patterns of industrial equipment. however, traditional static learning paradigms confine their application scenarios to independent and identically distributed data, unable to handle nonstationary data streams under changing working conditions. continual learning (cl) provides a solution for dynamic environments,

as one of the key components of rotating machinery, the health status of rolling-element bearings directly determines whether the equipment can function properly. due to the harsh working environment and the weak early degradation trends of rolling-element bearings, the extraction of degradation features and the accuracy of prediction are significantly challenged. to address this issue, a cumulative