Currently, the field of industrial monitoring provides a solid set of tools to optimize industry operations. This work proposes a method for predicting excessive vibrations of a gas turbine based on the Kalman filter optimized by fuzzy logic, using monitoring techniques to know their condition. It offers valuable information on the health status of the equipment, provide current performance indices and predict future indices expected by the operation of the equipment. The aim of this work is to develop a prognostic approach and propose modern techniques to best model the degradation of the gas turbine, in order to increase their safety and to deduce future decisions on the operating state of this machine.

We have explored sufficient conditions for the controllability of fractional-order Sobolev-type neutral functional integro-differential equations involving the Atangana-Baleanu-Caputo (ABC) derivative. Our approach integrates key concepts from semigroup theory, the contraction mapping principle, measures of noncompactness, and standard fixed point techniques. The theoretical results are further illustrated through a concrete example, validating the applicability of the proposed framework.

This paper investigates the stability and synchronization of Li system by combining Jacobi stability analysis within the Kosambi–Cartan–Chern (KCC) framework and inverse matrix projective anti-synchronization (IMPAS) scheme. First, we extend Jacobi stability criteria to a broad class of nonlinear dynamical systems and derive general conditions that complement conventional Lyapunov analysis. Second, we propose an IMPAS controller that simplifies the synchronization of chaotic systems through an arbitrary scaling matrix. Numerical simulations confirm the theoretical predictions and illustrate the practical effectiveness of the method. The results deepen our geometric understanding of stability and broaden the tool for controlling real-world nonlinear systems.

This study focuses on controlling a Fractional-Order Direct-drive Permanent Magnet Synchronous Generator (FOD-PMSG) in a wind turbine using an H${\infty}$ controller, which includes a mathematical definition of constraints related to the desired closed-loop behavior. The main advantage of this method lies in its ability to integrate classical control techniques with robust control in a cohesive framework. By utilizing fractional calculus and solving Linear Matrix Inequalities (LMIs), the system is effectively controlled and stabilized at the equilibrium point. Numerical results illustrate the effectiveness of the approach, and an electronic implementation is proposed to further validate these findings.

Offshore wind turbines are particularly vulnerable to perturbations and uncertainties, due to the dynamic and unpredictable nature of the wind, ocean currents, and environmental factors. These external disturbances can introduce chaotic behavior, leading to instability in the turbine's control system, reducing efficiency and performance. The chaotic dynamics, often caused by fluctuating wind speeds and variable sea conditions, make it challenging to maintain stable and reliable operation. To address these challenges, advanced control strategies, such as sliding mode control (SMC) combined with passivity theory, can be employed. This approach mitigates chaotic behavior by ensuring the system remains stable even under the influence of external disturbances. By leveraging the robustness of passivity-based control, the strategy accommodates variations in wind speed, turbulence, and mechanical uncertainties, ensuring optimal turbine performance and reliability in the face of perturbations. Simulation results indicate that this method can effectively handle chaotic dynamics, maintaining desired operational parameters despite the inherent uncertainties of the offshore environment.

In coal mine production, real-time monitoring of coal flow on conveyor belts is of great significance for ensuring production safety and improving transportation efficiency. Existing coal flow detection methods primarily rely on parameters such as coal width, belt speed, and coal flow depth, employing volume modeling or mass estimation. These methods face challenges such as complex sensor deployment and high costs. Therefore, this paper proposes a coal flow detection method based on a Feature Pyramid Network (FPN), which models the dynamic changes in coal flow on the conveyor belt and determines the duration to achieve intelligent coal flow detection. The backbone network incorporates Partitioned Multi-head Self-Attention (PMSA) to enhance local modeling capabilities. The FPN structure includes Adaptive Fine-Grained Channel Attention (FCA) modules and Convolutional Block Attention Module (CBAM), effectively preventing information loss and enhancing responsiveness to critical spatiotemporal information. Experimental results demonstrate that this method achieves good detection performance across various scenarios, providing a decision-making basis for intelligent speed control of conveyor belts.

In this paper, periodic motions in a periodically forced Duffing oscillator with damping switching are obtained semi-analytically through the implicit mapping method. Between system switching, specific dynamical subsystems in the switching system are continuous, which are discretized to obtain discrete implicit mappings. Based on discrete implicit mappings, specific mapping structures are employed to determine periodic motions in such a switching system, and the corresponding stability and bifurcations of periodic motions in the switching system is determined through the eigenvalue analysis. From the analytical solutions, initial conditions are chosen for numerical simulations. The numerical and analytical solutions of stable periodic motions match very well. However, for unstable periodic motions, numerical solutions move away from the analytical solutions once the computational time becomes longer. The method presented in this paper can be applied to other switched nonlinear systems, system controls and MEMS.