Acoustic-Array Diagnosis of Coupled Planetary Gearbox Faults Under Controlled 3D Spatial Interference with Physically Motivated Structural Priors

Non-contact acoustic arrays offer a complementary route for planetary-gearbox monitoring, but multiple sound sources introduce spatial ambiguity and masking. We propose a multi-modal mixture-of-experts framework (MM-MoE) with physically motivated structural priors for 16-channel acoustic signals. Four learned prototype waveforms replace per-channel wavelet transforms, and an adapted Vision Mamba encoder produces channel-aligned spectral embeddings. A waveform branch models temporal patterns, a coupling prior learned from inter-channel spectral similarity biases cross-modal attention, and task-aware expert routing supports simultaneous scene parsing and fault diagnosis. Test-bench gearbox recordings were replayed through two loudspeakers at controlled positions, heights and sound-pressure levels in a semi-anechoic chamber. Over five training runs, MM-MoE achieves window-level accuracies of 89.4% for 16-class spatial-energy scene parsing and 76.5% for 16-class coupled fault diagnosis, with 69.2% joint exact match (JEM), 13.4 percentage points above the strongest evaluated baseline (95% confidence interval: 12.4–14.4). Aggregating the windows of each recording raises JEM to 87.1%. When source regions are excluded from training, ordered fault accuracy on them is 68.3%, compared with 75.5% on seen regions. The findings are limited to controlled loudspeaker replay; validation on operating machinery remains necessary.