A Hybrid Approach to Fault Diagnosis and Lifespan Prediction in Complex Computer Systems
DOI:
https://doi.org/10.15837/ijccc.2026.1.6873Keywords:
complex computer systems, bat algorithm, hidden markov model, reliability, remaining lifespan, fault diagnosisAbstract
The increasing integration of software and hardware in modern computer systems has introduced significant reliability challenges, necessitating advanced fault diagnosis and lifespan prediction techniques. This study proposes a hybrid approach leveraging an improved Bat Algorithm (BA) and Hidden Markov Model (HMM) to enhance fault detection and system longevity assessment. By incorporating a genetic competition mechanism, the enhanced BA improves fault classification accuracy, while the HMM-based lifespan prediction model provides superior forecasting precision. Experimental results demonstrate that the proposed fault diagnosis model achieves a 95.68% accuracy and an area under the curve (AUC) of 96.83%, significantly outperforming conventional methods. Moreover, the lifespan prediction model surpasses backpropagation neural networks (BPNN), achieving a lower root mean square error (RMSE) of 0.021, indicating higher predictive reliability. These findings contribute to improving the stability and security of complex computing environments while reducing maintenance costs. Future work will focus on optimizing computational efficiency and extending real-time applications in large-scale systems.
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