Parameter variation caused by manufacturing tolerances and thermal drift makes PMSM fault-severity estimation difficult, because motor-level offsets and fault effects are coupled in the d–q model. This paper proposes a channel-selective BO-Fusion-PINN for parameter-generalized fault diagnosis. A healthy reference window is first used to estimate motor-parameter deviations through an integral least-squares observer, avoiding neural extrapolation of these offsets. A diagnostic window is then processed by a physics-informed LSTM branch and a data-driven LSTM branch, and Bayesian optimization assigns separate fusion weights to stator-resistance degradation and permanent-magnet flux weakening. Experiments over parameter out-of-distribution buckets and non-ideal simulation settings show that the fused estimator consistently improves on either branch alone. The method is especially effective in the flux channel and remains competitive with high-capacity data baselines while preserving physical interpretability. The primary scientific contribution is an identifiability-guided fusion rule that assigns physics and data trust to each fault channel according to its statistical observability rather than through a single global weight; in practical terms, this yields a compact and interpretable estimator that transfers across the parameter-tolerance band of a machine class and can, in principle, support controlled end-of-line screening and scheduled diagnostic assessment under a matched-window acquisition protocol.