As a crucial energy provider in electronic systems, accurate monitoring and assessment of the health status of switching power supplies are crucial for ensuring efficient system operation. This research suggests a hybrid strategy that combines empirical mode decomposition (EMD) with a long short-term memory (LSTM) network to overcome the shortcomings of both data-driven and classic physics model-based methods in predicting complicated, non-stationary degradation signals. In the proposed method, EMD is employed to decompose degradation signals into multi-scale intrinsic mode functions. The relevant components are then selected and further processed, while the LSTM network captures long-term dependencies and nonlinear temporal dynamics in the time series. This approach enables accurate prediction of degradation trends in switch-mode power supplies. To verify the effectiveness of the proposed algorithm, a degradation simulation test platform for a switch-mode power supply is designed, and the fault injection method is developed. In order to simulate component-level degradation processes realistically, a fault-injection circuit is expressly made to mimic the degradation behavior of important components, especially capacitor degradation within the filtering module of the switch-mode power supply. Based on the constructed degradation experimental setup, experiments are conducted under degradation conditions to systematically validate the effectiveness of the proposed method.

Considering the correlated failure characteristics of multiple subsystems in an electromechanical integrated transmission system arising from load transfer, control loops, and functional coupling, this study introduces a Copula-based approach to model the internal dependency structure while preserving the marginal reliability models of individual subsystems. By mapping subsystem lifetime data into a unified probability space via cumulative distribution functions, the proposed method achieves decoupled modeling of marginal distributions and dependency structures. The process for creating failure time samples is described, and a Gaussian Copula is chosen to build the system-level joint failure model based on an examination of the dependency characteristics of several Copula families combined with engineering failure mechanisms. The dependency structure of the generated samples is validated using the Kendall τ rank correlation coefficient, and the results show good consistency with the predefined correlation matrix. Additional investigation finds high-risk areas of joint failure and reveals a significant coupling link between the drive motor controller and the motor drive subsystem. The results demonstrate that the Copula-based framework can effectively characterize system-level correlated failures, providing quantitative support for coordinated monitoring and maintenance decision-making.

The capacitance value’s degradation level in relation to its original value is first split into four state intervals in order to accomplish state recognition and degradation monitoring of switching power supply. This gives following state recognition jobs a clear labeling basis. A method combining ripple signal feature extraction with a deep learning classification model is proposed to enable rapid identification of the current state. The wavelet transform is applied to decompose the ripple signal at multiple scales, extracting its time-frequency domain feature maps to capture subtle dynamic characteristics during capacitor degradation. In order to categorize and identify feature maps of various states, a deep convolutional neural network model based on feature extraction is built using a residual network (ResNet) with its potent feature representation capabilities and residual learning mechanism. Finally, a ResNet-LSTM model is employed to predict the power supply’s degradation trend, with results demonstrating relatively accurate prediction performance.

Traditional reliability modeling techniques based on the independence assumption tend to overstate system reliability, according to this study, which examines the strong coupling characteristics among several subsystems in complex control systems. To address this issue, a reliability analysis method combining fault tree decomposition and Copula modeling is proposed. Firstly, the complex control system is structurally decomposed using a fault tree to identify key failure modes and minimal cut sets. Secondly, the Copula function is employed to characterize the non-independent correlation among subsystems, and the expression for the overall system reliability is derived. Finally, a specific type of complex control system is taken as a case study, and the reliability results under the independence assumption and Copula modeling are compared. The study confirms that the traditional modeling approach based on the independence assumption has a significant overestimation risk by demonstrating that the reliability decline of this kind of complex control system during task execution is more significant after taking the coupling effect into account than that calculated by traditional methods. This method provides effective theoretical support for the reliability assessment and optimal design of complex control systems.

A dual-stack amplified piezoelectric actuator that can be deployed inside the compressor casing was devised to improve the performance of the active flow control (AFC) system for aero-engine compressors. A multi-field coupled dynamic model accounting for operational conditions and preload forces was established based on the thermopiezoelectric constitutive equations and the generalized Hamilton’s principle. Supporting upstream and downstream components for the actuator were designed, culminating in the fabrication of a functional actuator prototype and a hardware-in-the-loop (HIL) performance verification platform. The test platform simulated compressor bleed environments with pressures of 0.1-0.5 MPa and temperatures up to 80 °C. The accuracy of the model was validated by experimental results showing that, under 1-200 Hz mixed-frequency signals, the average tracking errors between the test data and model predictions for the piezoelectric actuator were 2.5% and 4.1% at working conditions of 0.3 MPa, 55 °C and 0.5 MPa, 80 °C, respectively, with maximum errors of 4.3% and 7.1%. In AFC injection flow tests, the system achieved a peak flow rate of 59.6 g/s within 2.5 ms, confirming the high-frequency response characteristics of the piezoelectric actuation system and demonstrating the practical value of the AFC system in enhancing compressor performance.

This work proposes a prescribed adaptive finite-time control approach that takes the impacts of oil temperature into account in order to address the problem of low tracking control accuracy in oil-immersed electro-hydrostatic actuators (OI-EHAs) under large temperature ranges. Firstly, a dynamic thermal coupling model is established to estimate the unmeasurable oil temperature in real-time, integrating temperature dynamics into the controller architecture to reduce system complexity. Secondly, a finite-time observer (FTO) is designed to effectively suppress composite disturbances arising from temperature prediction bias, parameter perturbations, and external disturbances. By feeding back the estimated disturbance values, the system error converges within a finite time. The tracking error and its dynamic properties are constrained by the introduction of a prescribed performance function (PPF), which guarantees that the error stays within specified bounds. Finally, the backstepping method is employed to integrate the FTO and the PPF, forming a composite controller with temperature-sensitive parameters and mechanisms for compensating uncertainty disturbances. This method ensures the stability of the closed-loop system. Experimental results demonstrate that the proposed controller exhibits excellent performance under various operating conditions.

The Electro–Hydrostatic Actuator (EHA) is applied to drive the control surface in flight control system of more electric aircraft. In EHA, the Oil-Immersed Motor Pump (OMP) serves as the core as a power assembly. However, the compact integration of the OMP presents challenges in efficiently dissipating internal heat, leading to a performance degradation of the EHA due to elevated temperatures. Therefore, accurately modeling and predicting the internal thermal dynamics of the OMP hold considerable significance for monitoring the operational condition of the EHA. In view of this, a modeling method considering cumulative thermal coupling was hereby proposed. Based on the proposed method, the thermal models of the motor and the pump were established, taking into account heat accumulation and transfer. Taking the leakage oil as the heat coupling point between the motor and the pump, the dynamic thermal coupling model of the OMP was developed, with the thermal characteristics of the oil considered. Additionally, the comparative experiments were conducted to illustrate the efficiency of the proposed model. The experimental results demonstrate that the proposed dynamic thermal coupling model accurately captured the thermal behavior of OMP, outperforming the static thermal parameter model. Overall, this advancement is crucial for effectively monitoring the health of EHA and ensuring flight safety.