Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

This paper presents a constant-pressure hydraulic PTO system that can convert stored pressure energy into electrical energy at a stable speed through hydraulic motors and generators. A multi-chamber cylinder can be connected to the main power generation circuit by check valves, and the motor displacement can be controlled by a fuzzy controller to maintain the main power generation circuit under stable pressure. The hydraulic transformer can control the forces applied to the floater. The hydrodynamic parameters of the floater are calculated by AQWA, and the optimal PTO damping of the hydraulic system is analyzed as the target of transformer control. MATLAB/Simulink and AMESim are used to carry out the co-simulation. Three kinds of wave elevation time-series for the specific state are designed for the simulation. In the co-simulation, three approaches are carried out for the simulation including no control strategy, fuzzy control with a fixed transformer ratio, and fuzzy control with a variable transformer ratio. Under the fuzzy control with a fixed transformer ratio, the floater displacement and captured energy do not increase significantly, but the oil pressure fluctuation is very stable, which indicates that the fuzzy controller maintains the stability of the main power circuit. While under fuzzy control with a variable transformer ratio, the power generation is not larger than those under no control strategy or fuzzy control with a fixed transformer ratio, which proves that this hydraulic transformer concept is less efficient.

Wave energy, as a vast renewable resource, remains underutilized despite its high potential. The oscillating water column (OWC) is one of the most efficient way to harvest wave energy. Due to the randomness of ocean wave excitation, a control strategy is needed to keep the conversion efficiency of OWC at a certain level. In this paper, an adaptive model predictive control (AMPC) method is proposed to optimize the efficiency of the air turbine and improve the overall efficiency of the OWC. Experiments were conducted in a wave flume to obtain realistic wave data, which were fed into the AMPC model for simulations. Results indicate that AMPC-optimized turbine efficiency exhibits improved performance under regular wave conditions and significantly enhances efficiency within certain intervals under short-period irregular waves. However, as the wave period increases, optimization becomes less stable. Overall, the study concludes that the adaptive MPC model effectively optimizes turbine efficiency under most conditions, highlighting its potential for enhancing OWC performance.

This study presents a systematic analysis of the stability and roll characteristics of an Oscillating Water Column (OWC) wave energy buoy. By integrating theoretical derivation and AQWA simulation, the research identifies thirteen possible heeling states of OWC buoy, focusing on five representative states applicable to the current design. A novel segmented-integration model is proposed to compute the centre of buoyancy and righting moment for the hollow-annular OWC buoy, accurately capturing the evolution of static and dynamic stability across heel angles from 0° to 90°. Results show that the buoy has an initial metacentric height of 0.33 m, a maximum righting arm of 0.713 m, a limiting static heel angle of 77°, and a minimum capsizing moment of 22,887 N·m—all significantly exceeding regulatory requirements. The roll natural period ranges from 5.8 to 7.7 s, with a tuning factor above 1.3, effectively avoiding resonance with typical wave periods in the target sea area. The buoy demonstrates excellent dynamic stability and capsize resistance. This study fills a gap in OWC buoy stability analysis and provides a practical guidance for the safe design of wave energy devices.

Single-point mooring systems are among the key systems for offshore oilfield development. The fluid swivel is a core component of such systems, enabling fluid transfer while allowing the vessel to follow the weather vane effect. The spring-energized seal is critical for ensuring reliable fluid transmission. Existing studies on spring-energized seals primarily focus on small-scale mechanisms, with limited research on large-scale seal design under complex operating conditions. This work investigates the dynamic sealing performance of the oil-transferring rotary joint in a 300,000 ton VLCC catenary single-point mooring system. A spring-energized seal is designed with a PTFE-based composite as the sealing jacket and Inconel 718 as the spring material. A finite element model of the spring-energized seal is developed in ANSYS 2022 R1, and the design is optimized to achieve lower equivalent strain, more uniform contact pressure distribution, larger contact width, and reduced friction. Fatigue life analysis of the optimized design verifies its reliability over a 10-year service period. The proposed study provides a reference for the design of dynamic seals in high-end offshore engineering equipment.