Data Fusion and Machine Learning for Diagnosing Electrical and Mechanical Faults in BLDC Motors

One of the main challenges in BLDC motor diagnostics is the identification of faults with different physical origins, especially in mixed states where the symptoms of multiple faults may overlap. In this work, a classification system based on feature-level data fusion was developed by combining current and rotational signals. A homogeneous Stacking Ensemble model was used as the main mechanism for fault classification. The study was conducted on a dataset of 184 samples representing four operating conditions: healthy operation, mechanical faults, electrical faults associated with permanent magnet degradation, and their combined occurrence. The stability of the proposed classifier was evaluated using ten different data splits. The experiments showed that omitting PCA preserves more diagnostically relevant information contained in the raw features, resulting in a classification accuracy of 97.3% with a standard deviation of 0.017. PCA consistently reduced performance across all considered data modalities. The model was further analysed using SHAP, indicating that its decisions were driven by physically interpretable features from both the rotational and current domains.