Support Vector Machine Based on Non-Linear Grey Wolf Optimizer for Maintenance Prediction

Maintaining aircraft engines is one of the most operationally and financially difficult challenges facing the aerospace industry, since a failure that goes undetected can cause an entire fleet to be grounded at a high cost. This paper presents an approach that combines Support Vector Machines (SVM) with a Non-Linear Grey Wolf Optimizer (NL-GWO) in order to improve predictive maintenance techniques for aircraft engines. The method makes use of the SVM’s strength in carrying out classification tasks and at the same time employs the NL-GWO to optimize the SVM’s hyperparameters C and γ; the NL-GWO is a Gaussian-decay version of the Grey Wolf Optimizer, which improves both exploration and exploitation. Experiments conducted on the NASA CMAPSS aircraft engine dataset show that the SVM optimized with NL-GWO attains an accuracy of 94.98%, an F1-score of 83.07%, and an AUC-ROC of 98.16%, outperforming the standard GWO-SVM across the majority of evaluation metrics. These results indicate that the use of machine learning in conjunction with metaheuristic optimization is viable for the development of intelligent predictive maintenance systems. Indeed, the results from the experimentation, which involves ablation tests of the decay function, statistical tests over ten iterations, and comparison with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), prove that the proposed method is reliable and practical.