ETNeXt: Integrated feature engineering and classification framework for BLDC motor fault detection

Motor fault detection is critical for industrial reliability, with acoustic signals serving as a key diagnostic tool. This study employs the ETNeXt framework for robust motor acoustic condition classification using a dataset of 2,021 wav files categorized as "good," "broken," or "heavyload." The methodology includes: (i) applying a 7-level Multilevel Discrete Wavelet Transform (MDWT) with the 'sym4' wavelet to extract approximation coefficients; (ii) using the Triadic Signal Kernel-Local Histogram Pattern to generate feature histograms based on signum, upper ternary, and lower ternary functions; (iii) selecting optimal features via Neighborhood Component Analysis (NCA) and Chi-square (Chi2) methods, with 35 features per level over 5 iterations; and (iv) classifying feature vectors using Fine k-NN and Cubic SVM with 10-fold cross-validation. The ETNeXt framework achieved 99.80% accuracy with Fine k-NN and 100% with Cubic SVM, demonstrating exceptional performance. On a new dataset, it maintained high generalizability, achieving 99.95% accuracy with Cubic SVM and 99.16% with Fine k-NN. Additionally, integrating a cochlear-inspired transformation and ResNet50 model yielded 99.95% accuracy with Fine k-NN and 97.97% with Cubic SVM, further enhancing diagnostic capability. These results highlight the ETNeXt framework's effectiveness and robustness for motor fault detection and classification.