Marinai, L., Probert, D., and Singh, R., Prospects for aero gas turbine diagnostics: A review, Appl. Hu, X., Vian, J., Slepski, J.R., and Wunsch, D.C., Vibration analysis via neural network inverse models to determine aircraft engine unbalance condition, Proceedings of the International Joint Conference on Neural Networks, 2003, vol. Naeem, M., Singh, R., and Probert, D., Consequences of aero-engine deteriorations for military aircraft, Appl. and Yoneyama, T., Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles, IEEE Syst. The WOANN achieved overall prediction accuracy of 95%, thus presenting itself as a very useful tool for day-to-day monitoring of aircraft engine health using the data downloaded from the aircraft’s HUMS. The values obtained for the metrics of Accuracy, Error, False Positive Rate, F1 score, Mathews Correlation Coefficient, Specificity, Kappa coefficient are found to be the best for WOANN algorithm. The results show that WOANN algorithm classifies and predicts engine health far more accurately as compared to PNN, KNN and BPANN. The efficiency of the WOANN technique for engine health monitoring, is compared with that of three other common machine learning algorithms: Probabilistic based Neural Network (PNN), K-Nearest Neighbour (KNN), and Back propagation based Artificial Neural Network (BPANN). Thirteen engine parameters have been used to determine and monitor the health of the engine. The actual engine data recorded during 47 different flights of eight different engines (of the same type) have been considered in this work. This paper proposes a novel, simple method for monitoring aircraft engine health using Whale Optimization Algorithm based Artificial Neural Network (WOANN) technique, for analyzing the data downloaded from the health and usage monitoring system (HUMS) of a military aircraft. Health monitoring of an aero-engine assumes importance in the light of primary requirements of flight safety and reliability.
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