In this paper, we present a novel and unified model for studying the vibration of cylindrical shells based on the three-dimensional (3D) elastic theory and the Carrera Unified Formulation. Our approach represents a significant advancement in the field, as it enables us to accurately predict the vibrational behavior of cylindrical shells under arbitrary boundary conditions. To accomplish this, we expand the axial, circumferential, and radial displacements of the shell using Chebyshev polynomials and Taylor series, thereby reducing the dimensionality of the expansion and ensuring the precision and rigor of our results. In addition, we introduce three groups of artificial boundary surface springs to simulate the general end boundary conditions of the cylindrical shell and coupling springs to strongly couple the two surfaces of the cylindrical shell φ = 0 and φ = 2π to ensure continuity of displacements on these faces. Using the energy function of the entire cylindrical shell model, we obtain the characteristic equation of the system by finding the partial derivatives of the unknown coefficients of displacement in the energy function. By solving this equation, we can directly obtain the vibration characteristics of the cylindrical shell. We demonstrate the convergence, accuracy, and reliability of our approach by comparing our computational results with existing results in the literature and finite element results. Finally, we present simulation results of the frequency features of cylindrical shells with various geometrical and boundary parameters in the form of tables and figures. Overall, we believe that our novel approach has the potential to greatly enhance our understanding of cylindrical shells and pave the way for further advancements in the field of structural engineering. Our comprehensive model and simulation results contribute to the ongoing efforts to develop efficient and reliable techniques for analyzing the vibrational behavior of cylindrical shells.

With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.

As a type of transmission actuator commonly used in mechanical structures, high-speed rotary mechanisms are widely used in the machinery, energy, civil engineering, and military industries. The main working part of the mechanism is the end face, and the vibration characteristics of the end face directly affect the accuracy and completeness of the whole mechanism. In terms of performance, the end face vibration characteristics reflect the degree of change in the machine’s end face displacement. The traditional end face displacement test method uses a single contact sensor for measurement, but the degree of error in the system is large. It is also difficult to adapt this method to the current high-precision working conditions under the requirements for accurate vibration prediction and control technology. Therefore, this work proposes a test method based on multi-sensor acquisition and data fusion, using different sensors to form a sensor test network. It seeks to address the limitations of the conventional test method that relies on a single-frequency domain, using high-frequency domain impact signals, medium-frequency domain vibration signals, and low-frequency domain rotational speed signals for multi-sensor data fusion to achieve a complete high-speed rotating mechanism with a particular focus on full-area coverage. In order to better obtain the test data, the sensor layout network is optimized through the optimization design method, and the optimal sensor installation scheme is proposed. Finally, the data fusion algorithm and sensor layout scheme are experimentally verified with a typical high-speed rotating mechanism.

High-overload testing equipment is a key platform for evaluating mechanical performance under extreme conditions, requiring specialized functional design to meet stringent operational demands. The current design process faces numerous challenges, including overreliance on designers’ experience, incomplete requirement analysis, and insufficient automation, resulting in suboptimal solutions. To address these issues, in this study, we propose an integrated method based on axiomatic design (AD) and the Theory of Inventive Problem Solving (TRIZ). This method systematically decomposes technical specifications, maps requirements to a structural framework, and resolves design conflicts using inventive principles. The method employs a comprehensive evaluation framework combining the analytic hierarchy process (AHP) and quality function deployment (QFD) to quantitatively assess candidate designs. It facilitates the development of efficient, standardized high-load testing equipment solutions, enhancing design reliability and innovation capabilities.

Transient contact responses in confined guide channels contain sharp events and parameter-dependent phase shifts, which make fixed-output full-history surrogates difficult to train. This study develops a time-conditioned residual surrogate (TC-ResNet) to propagate uncertainty in the bounded center-of-mass eccentricity components into the transient acceleration responses of a generic pressure-driven, high-overload axisymmetric body. The nonlinear reference model includes prescribed base pressure, wall contact and impact, velocity-dependent friction, gravity, pitch and yaw, and eccentricity. TC-ResNet predicts one response value for each parameter–time query by combining normalized physical parameters with Fourier-embedded time. The axial acceleration is modeled directly, whereas low-frequency radial trends and sliding root-mean-square (RMS) curves represent dominant lateral motion and local vibration intensity. On a common 108-case test set, TC-ResNet achieved the highest coefficient of determination (R²) for a_x^low (0.850), a_x^rms (0.839), and a_z^low (0.801), as well as the highest macro-mean R² (0.848). The fixed-output multilayer perceptron (MLP) remained best for a_y (0.995) and a_z^rms (0.775), demonstrating that the proposed model is not uniformly superior. Interval analysis shows that the prescribed pressure load limits axial sensitivity, whereas radial offsets alter eccentric pressure moments and wall contact; the axial offset primarily changes contact and friction moment arms, and inclination mainly affects the later radial response through gravity decomposition and accumulated contact differences. Accuracy approaches a plateau between 378 and 504 training cases. On the same central processing unit (CPU), TC-ResNet requires 0.081 s per curve (approximately 170× faster than the reference solver), and graphics processing unit (GPU) inference requires 0.019 s per curve. The reported envelopes support qualitative sensitivity analysis, but experimental calibration and validation remain necessary.

Formations of fixed-wing unmanned aerial vehicles (UAVs) must maintain mission-level coordination despite actuator degradation, bias faults, model uncertainty, wind disturbances, and command limits. This study presents a mission-oriented formation-recovery framework. Acceptable relative position and velocity performance is represented by an ellipsoidal tolerance set, and resilient formation recovery time (RFRT) is the elapsed time from fault onset to the first permanent re-entry into that set. The controller generates relative demand commands that are reconstructed from the parent limited command and then clipped componentwise. A second-order edge-based extended state observer, driven by the relative limited command, estimates the physical edge fault–disturbance mismatch, while the command-saturation residual is retained explicitly in the closed-loop analysis. A distributed fault-tolerant controller combines linear recovery feedback, observer-based compensation, and a continuous robust term. The analysis uses a finite Lyapunov energy-jump inequality at step-fault instants and an upper-right Dini derivative at saturation breakpoints to establish uniform ultimate boundedness, a sufficient recovery-set inclusion condition, and an RFRT upper bound. In the one-leader–four-follower baseline simulation, permanent re-entry occurs at 22.3313 s, corresponding to an RFRT of 2.3313 s. Relative to Edge-ESO-FTC, the proposed method reduces the post-fault peak metric by 61.2%, RFRT by 25.2%, cumulative position deviation by 54.6%, and out-of-bounds duration by 67.7%.