Call For Paper Volume: V, Issue: 08 | AUGUST 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume II | Issue 6 | 2023 | Paper ID: IJATEM23JUNE003

Harris Hawks Optimized Mamba-Transformer Framework for Intelligent Aircraft Engine Fault Diagnosis

, R. Sahila Devi

Accurate and early Aircraft Engine Fault Diagnosis (AEFD) is important for improving flight safety, reducing maintenance costs, and preventing unexpected engine failures. However, the engine is exposed to continuous mechanical and thermal stresses, it disposed to various types of faults and performance degradation. To overcome these problems, this paper proposes a Harris Hawks Optimized Mamba-Transformer (HHO-Mamba-Transformer) framework for intelligent AEFD. Proposed Mamba-Transformer model, where the Mamba module efficiently captures long-range temporal dependencies in aircraft engine operating conditions, whereas the Transformer module learns important global relationships and interactions between the extracted features. The HHO algorithm is developed to optimize the Mamba-Transformer parameters, thereby improving learning efficiency, convergence, and diagnostic performance. Experimental evaluation is implemented in Python software using NASA C-MAPSS-1 Turbofan Engine Degradation Dataset with 98.9% of accuracy, recall, F1-score and 99% of precision for accurate detection of AEFD. The novelty in this paper is the proposed HHO-Mamba-Transformer framework provides an intelligent, adaptive, and efficient solution for AEFD.

Aircraft Engine Fault Diagnosis, Harris Hawks Optimization, Mamba, Transformer

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