Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis

This study compares a conventional feature-based representation and a physics-guided condition-index (CI) representation for vibration-based fault diagnosis of industrial motors. Both representations were constructed from identical vibration signal segments and evaluated under the same classification conditions using support vector machine (SVM) classifiers. For each representation, a genetic algorithm (GA) was repeatedly applied to the training data to select three representative variables, after which the SVM hyperparameters were optimized using three-fold cross-validation. Permutation Importance and SHapley Additive exPlanations (SHAP) were subsequently used to interpret the contributions of the selected CIs. Independent test motors, whose fault conditions had been established through manufacturer troubleshooting before the present analysis, were excluded from all model-development procedures. For the independent Unbalance and Misalignment test motors, the CI-based model achieved segment-level classification rates of 99.83% and 100%, respectively, whereas the conventional representation showed substantial misclassification. Because these segments originated from a single physical motor for each fault condition, the reported rates represent within-motor segment-level outcomes rather than population-level estimates of diagnostic performance. FFT analysis revealed dominant 1X and 2X components in the corresponding test data, consistent with their established fault conditions. For an additional independent motor identified as Air-gap Unbalance, the CI-based model classified all test segments as Air-gap Unbalance, while the FFT spectrum exhibited fractional-frequency characteristics similar to those observed in the corresponding fault data. Overall, the physics-guided CI representation produced classification outcomes that were more consistent with the established fault conditions and provided a more physically interpretable basis for model decisions under the industrial motor conditions examined in this study.