Machine fault diagnosis from vibration requires learning from scarce labelled fault recordings while meeting the computational constraints of edge devices for local inference.
We introduce DualRes, a compact oscillatory state-space model that combines two complementary spectral views of vibration, capturing rapid changes and fine frequency structure.
Time-aligned views are processed by selective oscillatory memory, which learns how long to retain temporal patterns.
The encoder contains 39,528 parameters.
We evaluate supervised learning across six bearing datasets and a gearbox benchmark, with an additional gearbox pilot.
Recording-level splits and explicit accounting of labelled duration distinguish data efficiency from repeated exposure to correlated samples.
On the main gearbox benchmark, DualRes achieves state-of-the-art performance among the nine evaluated methods at six of seven label budgets.
With about six labelled seconds per class, it improves macro-F1 by 16.1 percentage points over the next strongest comparator.
On the same benchmark, DualRes achieves a 1.44-fold recording-level speedup and a 24.8-fold reduction in checkpoint storage relative to a selective state-space baseline under matched hardware and runtime conditions.
Bearing results reveal task-dependent trade-offs.
These findings support oscillatory memory as a compact approach to vibration diagnosis under limited labelled exposure.