Dual-View Complementary Multiscale Entropy Fusion for Rotating-Machinery Fault Diagnosis

Entropy-based fault diagnosis depends on signal representation and the properties captured by each entropy measure. We evaluated dual-view complementary multiscale entropy fusion (DCMEF), which combines RMS-normalized raw vibration with a normalized logarithmic Hilbert envelope. Three entropy measures at five fixed scales produce 30 candidate features, with imputation, selection, and classifier tuning confined to grouped training folds. In an exploratory comparison against 20 representations across four selected tasks and three classifiers, DCMEF achieved the highest observed accuracy in nine of 12 settings and tied for highest in three. A separate view ablation found that the full bank outperformed raw-only features in two settings, tied in five, and underperformed in five; relative to envelope-only, it outperformed in ten and tied in two. Thus, raw-vibration features were useful across these settings, whereas adding envelope features had inconsistent effects. Independent evaluation is needed to assess performance beyond the selected tasks.