Physics-Guided MCSA–IPSA Fusion and Low-Data Parametric RUL Estimation for Induction Motors: A Simulation-Based Study

Electrical-signature methods offer a non-invasive basis for induction-motor condition monitoring, but multimodal fusion does not necessarily improve prognosis when modalities have unequal reliability. This simulation-based study combines Motor Current Signature Analysis (MCSA), Instantaneous Power Signature Analysis (IPSA), reliability-weighted fusion, an interpretable health indicator, low-data Remaining Useful Life (RUL) estimation, measurement-noise analysis, and computational timing. A verified phase-coordinate induction-motor model was evaluated for healthy operation, progressive stator asymmetry, and reduced-circuit broken-rotor-bar (BRB) cases. For stator asymmetry, the reliability-weighted health trajectory was strictly directional in all 72 evaluated states. Reliability weighting improved over equal fusion and IPSA-only prognosis but did not outperform MCSA-only RUL accuracy. Across all diagnostic inputs, a free-intercept linear model outperformed the exponential model; MCSA-LIN achieved the lowest clean normalized-RUL MAE and RMSE of 0.082690 and 0.084853, respectively. Under synthetic electrical noise, p05–p95 intervals contained the clean estimate in all 360 cells but the synthetic truth in none, showing that measurement-noise dispersion was much smaller than deterministic bias. For BRB diagnosis, slip-tracked current sidebands were primary and low-frequency power signatures were complementary. The worst complete stator-diagnostic p95 latency was 6.350665 ms for a 2 s acquisition window. These results provide a reproducible simulation-only baseline for subsequent experimental validation.