With the increasing complexity of offshore wind turbine structures and the rapid expansion of wind power projects, efficient, reliable, and robust fault diagnosis and condition monitoring methods have become crucial for effective operation and maintenance management. Wind turbine condition monitoring plays a pivotal role in improving operational efficiency. However, most existing fault diagnosis techniques based on vibration signals are designed for rotating mechanical equipment operating at constant speeds. In contrast, offshore wind turbines experience continuously varying speeds, especially during start-up, shutdown, and under fluctuating wind conditions, leading to rotor speed variations that complicate monitoring. This paper presents a comprehensive analysis of the vibration and fault characteristics of key components in the main drivetrain of offshore wind turbines, with a particular focus on monitoring non-stationary (variable speed) operations. Unlike conventional approaches, this work specifically addresses the challenges posed by the dynamic operating conditions of offshore wind turbines, providing insights into multi-component vibration signal feature extraction and fault diagnosis under variable-speed scenarios. The comparative analysis offered in this paper highlights the limitations of current methods and outlines key directions for future research, emphasizing practical solutions for fault diagnosis and condition monitoring in offshore wind turbine operations under variable-speed conditions. This study not only fills a gap in the current literature but also provides valuable guidance for enhancing the reliability and efficiency of offshore wind turbine maintenance.

Offshore wind-turbine gearboxes operate under continuously varying speeds, while incipient planetary-gear fault signatures are weak, modulated, and difficult to recover without a tachometer. This study presents a tachometer-free framework that separates low- and high-oscillation components using adaptive oscillatory behavior signal decomposition, with its Q-factor and sparsity parameters selected by a convergence index and multi-scale periodic-modulation-intensity double loop. The integrated workflow uses existing gearbox vibration channels for phase estimation and order analysis when a tachometer signal is unavailable. Its contribution is the coupling of adaptive component selection, compact high-order time–frequency analysis and ridge-based resampling. A downsampled fourth-order synchrosqueezing transform (DSST4) produces a compact, concentrated time–frequency representation. Penalized global-path ridge extraction then estimates shaft speed, and segmented integral-approximation resampling converts the signal to the angle domain. For a simulated amplitude- and frequency-modulated signal at −5 dB, DSST4 reduces Rényi entropy from 7.35 to 5.65 and computation time from 4.959 s to 0.002 s, relative to the full-sampling fourth-order transform. In the University of Ottawa variable-speed bearing dataset, speed-estimation error is 18.67 rpm. For a 2 MW offshore wind turbine, order spectra identify a ring-gear component near order 0.714 in March, and an additional planet-gear component near 0.456 with modulation sidebands in April. Teardown confirmed spalling on both gears.