Diagnosis-Oriented GNA–VMD Parameter Selection for Planetary Gearbox Fault Diagnosis: A Record-Level Cross-Condition Evaluation

Planetary gearbox diagnosis is challenged by nonstationary vibration, acquisition-condition variation, and noise. This study proposes a GNA-VMD temporal convolution–Transformer (GNA-VMD-TCT) method. The Geomagnetic Navigation Algorithm jointly optimizes the VMD mode count and penalty factor using a diagnosis-oriented fitness function that combines envelope entropy, modal redundancy, Fisher class separability, and a mode-count penalty. The resulting multi-channel intrinsic mode functions are processed by a temporal convolution–Transformer network integrating multi-scale convolution, gated temporal convolution, Transformer encoding, and cross-gated fusion. Under MAT-file-level splitting and source-validation-based checkpoint selection, five-seed cross-condition Macro-F1 was 31.33 ± 8.12% for Condition 1 → 2 and 29.68 ± 5.73% for Condition 2 → 1, with no consistent advantage over the baseline methods. Retrospective within-condition tests favored Raw-TCT, and operator-level ablations did not confirm consistent gains from every network component. Gaussian-noise tests using fixed seed-0 checkpoints and three noise realizations per SNR likewise showed no consistent robustness advantage. Although the revised runs excluded target data from GNA optimization, network training, and checkpoint selection, target results had been examined during earlier exploratory work, so the evaluation was not fully blind. These findings characterize the limitations of diagnosis-oriented VMD parameter selection under acquisition shifts and noise.