The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

The six-degree-of-freedom (6 DOF) motion of the mother-ship, which is caused by wave fluctuation, makes offshore operations difficult. To solve this problem, various heave compensation systems were proposed, as well as the law of wave motion. These heave compensation systems included passive heave compensation system, semi-active heave compensation system, and active heave compensation (AHC) system. However, the empirical formula of wave motion is not suitable for practical offshore operations, and rare AHC systems take the characteristic of compensation into consideration. To study the compensation characteristic of the AHC system based on electric-driven marine winch in the actual sea condition and improve the accuracy of compensation, this work uses the measured data of wave motion, and deduces the motion equations of multi-axial driving system. By substituting the above elements into the AHC system and designing the sliding mode controller (SMC), the simulation results show the ideal compensation effect of the system. By comparing with experimental results, it is concluded that the driving system operates in according with the specified states, and the compensation performance of system is effective. The overall work is conductive to the research on AHC system, as well as the exploitation of marine resources.

The six-degree-of-freedom (6 DOF) motion of the mother ship, especially the heave motion, brings difficulties to offshore operations and even causes safety accidents. To ensure the reliability of the launch and recovery system when the active heave compensation (AHC) function works, its dynamic characteristics are analyzed. Firstly, the dynamic model of the launch and recovery system with a rigid rope is constructed, and the mathematical simulation of the launch and recovery system without the AHC function under the excitation of irregular waves is completed. Secondly, a flexible rope based on the finite element method is established, and the dynamic responses of the launch and recovery system with AHC function under different conditions are compared with those without AHC function. The simulation results demonstrate that the dynamic responses of the system are related to the load, the underwater penetration, and the sea condition. In detail, the increase of the load leads to an increase in the dynamic response of the system. In essence, the underwater penetration affects the dynamic response of the system by affecting the activity of load acceleration. Therefore, a short rope is preferred in the maritime operations. Moreover, the harsh sea condition usually causes an undesirable effect of AHC function. In summary, the overall work is conductive to the modeling of the launch and recovery system, as well as the development of AHC technology so as to improve the quality of offshore operations in the future.

The finite element model of a dual-rotor system was established by Timoshenko beam element. The dual-rotor system is a coaxial rotor whose supporting structure is similar to that of an aero-engine rotor system. The inner rotor is supported by three bearings, which makes it a redundantly supported rotor. The outer rotor connects the inner rotor by an intershaft bearing. The spectrum characteristics of the dual-rotor system under unbalanced excitation and misalignment excitation were analysed in order to study the influence of coupling misalignment of the inner rotor on the spectral characteristics of the rotor system. The results indicate that the vibration caused by the misaligned coupling of the inner rotor will be transmitted to the outer rotor through the intershaft bearing. Multiple harmonic frequency components, mainly 1x and 2x, will be excited by the coupling misalignment. The amplitudes of all harmonic frequencies increase with the misalignment in both the inner and outer rotors. The vibration level of the outer rotor affected by the misalignment is lower than that of the inner rotor because it is far from the misaligned coupling. Harmonic resonance occurs when any harmonic frequencies of the misalignment response coincide with a natural frequency of the system. In order to verify the theoretical model, experiments are performed on a test rig. Both the experimental and simulation results are in good accordance with each other.

Accurate evaluation of the state of health (SOH) of lithium-ion batteries (LIBs) is essential for ensuring their reliability and safety during operation. This study proposes an interpretable data-driven framework that integrates electrochemical impedance spectroscopy (EIS) measurements with features derived from an equivalent circuit model (ECM) and distribution of relaxation times (DRT). To mitigate temperature-induced spectral scaling while preserving degradation-related patterns, an in-line normalization strategy is applied to the impedance data. An Extreme Gradient Boosting (XGBoost) regression model is then trained using the fused feature set. On the cross-temperature test cells, the proposed method achieves an average coefficient of determination (R ² ) of 0.936, with a root mean squared error (RMSE) of 1.08% and a mean absolute error (MAE) of 0.77%, which significantly outperforms the unnormalized baseline model (R ² = 0.874, RMSE = 1.63%, MAE = 1.26%). Shapley additive explanations (SHAP) indicate that the model relies primarily on low-to-mid frequency interfacial features associated with SEI evolution and charge-transfer processes, supporting a physically meaningful interpretation of the prediction mechanism. These results suggest that the proposed approach enables temperature-robust and interpretable SOH estimation for LIBs across temperatures.