Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model

To address the limitations of traditional predictive maintenance for large wind turbines, a fault prediction method that combines a gated recurrent unit (GRU) network with an improved ant lion optimization (IALO) algorithm is proposed. Traditional fault monitoring primarily relies on the supervisory control and data acquisition (SCADA) system to monitor parameters such as oil temperature using threshold-based alarm methods. However, this approach suffers from low accuracy in judgment and delayed fault detection. To enhance the accuracy and timeliness of fault warnings, this paper selects SCADA feature variables using the Pearson correlation coefficient (PCC) and optimizes the hyperparameters of the GRU model using the IALO algorithm, which is enhanced by Latin hypercube sampling and random sampling ranking. The method is based on historical data during normal operation, and the residuals and normal distribution are used to set warning thresholds for fault prediction. The results indicate that this method overcomes the issue of traditional hyperparameter tuning falling into local optima and surpasses conventional methods in terms of prediction accuracy and timeliness. It can effectively improve the gearbox fault-warning performance.