Advancing bearing fault detection through a modified metaheuristic optimization approach

Introduction Detecting bearing faults plays a vital role in industrial maintenance since discovering problems early can help avoid unexpected breakdowns and expensive production losses. Yet, spotting these faults in their initial stages is still difficult because vibration signals are often complex and change over time. Methods In this study, optimized Mel Frequency Cepstral Coefficients (MFCC) feature extraction approach enhanced through a modified FOX optimization algorithm. The enhancement focuses on fine-tuning MFCC hyperparameters to maximize the discriminative power of extracted features for fault detection tasks. The proposed Enhanced FOX (EFOX) algorithm integrates different random distribution method and improved exploration–exploitation balance, enabling more effective parameter optimization compared to conventional methods. Results Experimental evaluations were conducted using benchmark datasets, and the optimized MFCC features were compared against those obtained via standard MFCC settings and other metaheuristic optimization techniques. Results demonstrate that our approach consistently outperforms competing methods in terms of classification accuracy and the robustness of the proposed model was assessed by testing it on two distinct bearing’s datasets with different noise ratios including −3 dB and −6 dB. Discussion The analysis highlights the impact of each of hyperparameter’s of MFCC to bearing fault detection.