Research on Engine Lubrication Oil Temperature Prediction Based on WOA-LSTM Algorithm

To enhance the reliability and exactness of forecasting aircraft engine oil temperatures, this study introduces a predictive model that combines the Whale Optimization Algorithm (WOA) with a Long Short-Term Memory Network (LSTM). The approach leverages advanced computational techniques to achieve greater accuracy in temperature estimation. This approach employs the Whale Optimization Algorithm to finetune both the hidden layer neuron count and batch size within the LSTM neural network framework. The resulting WOA-LSTM hybrid model is specifically designed to forecast lubrication oil temperature, with its predictive capabilities rigorously evaluated against a standard LSTM model through comparative analysis. Experimental findings reveal that the WOA-LSTM method outperforms conventional LSTM in temperature prediction accuracy, delivering superior precision in its results.