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When using a fiber optic gyroscope as the core measurement element in an inertial navigation system, its work stability and reliability directly affect the accuracy of the navigation system. The modeling and fault diagnosis of the gyroscope is of great significance in ensuring the high accuracy and long endurance of the inertial system. Traditional diagnostic models often encounter challenges in terms of reliability and accuracy, for example, difficulties in feature extraction, high computational cost, and long training time. To address these challenges, this paper proposes a new fault diagnostic model that performs a fault diagnosis of gyroscopes using the enhanced capsule neural network (iCaps NN) optimized by the improved gray wolf algorithm (NIGWO). The wavelet packet transform (WPT) is used to construct a two-dimensional feature vector matrix, and the deep feature extraction module (DFE) is added to extract deep-level information to maximize the fault features. Then, an improved gray wolf algorithm combined with the adaptive algorithm (Adam) is proposed to determine the optimal values of the model parameters, which improves the optimization performance. The dynamic routing mechanism is utilized to greatly reduce the model training time. In this paper, effectiveness experiments were carried out on the simulation dataset and real dataset, respectively; the diagnostic accuracy of the fault diagnosis method in this paper reached 99.41% on the simulation dataset; the loss value in the real dataset converged to 0.005 with the increase in the number of iterations; and the average diagnostic accuracy converged to 95.42%. The results show that the diagnostic accuracy of the NIGWO-iCaps NN model proposed in this paper is improved by 13.51% compared with the traditional diagnostic methods. It effectively confirms that the method in this paper is capable of efficient and accurate fault diagnosis of FOG and has strong generalization ability.

The electrical load and the magnetic load of the transverse flux permanent magnet linear motor (TFPMLM) are decoupled from each other, which makes it easy to achieve modularization, and it has broad application prospects in the field of low speed and high thrust. Firstly, the topology structure of a cylindrical TFPMLM is proposed, and its working principle is introduced. Secondly, the basic electromagnetic performance of the motor is studied. Thirdly, the influence of the structural parameters on the flux linkage, back-EFM, and cogging force has been analyzed by the 3D finite element method (FEM). Then, the kriging agent model is constructed through the DOE experiment, and a set of size parameters with better performance are found by using a multi-objective genetic algorithm. Finally, the prototype and experimental setup have been developed. The calculated results and the measured results are in good agreement.

According to the Magnus principle, a rotating cylinder experiences a lateral force perpendicular to the incoming flow direction. This phenomenon can be harnessed to boost the lift of an airfoil by positioning a rotating cylinder at the leading edge. In this study, we simulate flapping-wing motion using the sliding mesh technique in a heaving coordinate system to investigate the energy harvesting capabilities of Magnus effect flapping wings (MEFWs) featuring a leading-edge rotating cylinder. Through analysis of the flow field vortex structure and pressure distribution, we explore how control parameters such as gap width, rotational speed ratio, and phase difference of the leading-edge rotating cylinder impact the energy harvesting characteristics of the flapping wing. The results demonstrate that MEFWs effectively mitigate the formation of leading-edge vortices during wing motion. Consequently, this enhances both lift generation and energy harvesting capability. MEFWs with smaller gap widths are less prone to induce the detachment of leading-edge vortices during motion, ensuring a higher peak lift force and an increase in the energy harvesting efficiency. Moreover, higher rotational speed ratios and phase differences, synchronized with wing motion, can prevent leading-edge vortex generation during wing motion. All three control parameters contribute to enhancing the energy harvesting capability of MEFWs within a certain range. At the examined Reynolds number, the optimal parameter values are determined to be a∗ = 0.0005, R = 3, and ϕ0 = 0°.

A transverse flux linear motor is a special type of linear motor with a high thrust force density, and it has broad application prospects in the field of linear direct-drive systems. In the process of oil production, the vibration of the linear motor poses a significant amount of harm to the system due to its special slender structure. This paper focuses on the electromagnetic vibration of a transverse flux permanent magnet linear submersible motor (TFPMLSM). Firstly, the no-load air gap flux density is calculated based on the field modulation principle. Secondly, the radial electromagnetic force (REF) of the TFPMLSM is calculated, and the finite element method (FEM) is used to analyze the time-space and spectral characteristics of the REF. Then, the influence of secondary eccentricity on the frequency spectrum of the REF is further concluded. Finally, the natural frequencies of each vibration mode are calculated using the modal superposition method and the influence of the REF on the motor vibration is obtained through magnetic-structural coupling analysis. The research results found that the motor does not cause resonance at low speeds, and the fundamental frequency of REF has the greatest impact on electromagnetic vibration.

A novel linear permanent magnet vernier generator (LPMVG) for small-power off-grid wave power generation systems is proposed in this paper. Firstly, in order to reduce the cogging force and the inherent edge effect of the linear generator, a staggered tooth modular structure is proposed. Secondly, in order to improve the output power and efficiency of the LPMVG and reduce the fluctuation coefficient of electromagnetic force, the relationship between the parameters of the generator is studied, and a method combining multi-objective optimization and single parameter scanning based on the response surface model and particle swarm optimization algorithm is proposed to obtain the optimal structural parameters of the generator. Thirdly, the output power and efficiency of the optimized generator are calculated and analyzed based on the two-dimensional finite element method, and the effectiveness of the multi-objective optimization design method based on the response surface model and particle swarm optimization algorithm is verified. Finally, a prototype is developed, and the calculated results and the measured results are shown to be in good agreement.