The maintenance costs associated with offshore wind turbines, particularly those related to logistics and system downtimes, are significantly influenced by the reliability of hydraulic components, especially the pitch control system. Accumulator failures, which constitute a notable percentage of system faults, often result from gas leakage and pressure drops, highlighting the need for efficient fault detection and diagnosis (FDD) methods. This paper presents a novel approach utilizing Long Short-Term Memory (LSTM) neural networks for detecting faults in hydraulic accumulators. Two LSTM models were developed: a regression model that estimates the exact pre-charge pressure and a classification model that predicts pressure ranges. The models were trained and validated using both experimental and simulation data from a hydraulic test setup. Results demonstrated that the regression network achieved a root mean square error (RMSE) of approximately 4.2 bar, while the classification network reached 78.75% accuracy. The findings show that LSTM networks provide precision similar to prior art but for a larger variation of load cases. Thus, the proposed non-invasive method is promising for early fault detection in offshore wind turbine accumulators, potentially reducing operational costs and enhancing maintenance strategies.