{"slug": "when-labels-are-scarce-an-oscillatory-state-space-model-for-vibration-diagnosis", "title": "When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis", "summary": "Researchers introduced DualRes, a compact oscillatory state-space model with 39,528 parameters for machine fault diagnosis from vibration data, reporting state-of-the-art performance among nine evaluated methods at six of seven label budgets on the main gearbox benchmark. With about six labelled seconds per class, DualRes improved macro-F1 by 16.1 percentage points over the next strongest comparator, and achieved a 1.44-fold recording-level speedup and a 24.8-fold reduction in checkpoint storage versus a selective state-space baseline under matched hardware and runtime conditions. The work, posted as arXiv:2609.27411v1, evaluates supervised learning across six bearing datasets and a gearbox benchmark, with bearing results showing task-dependent trade-offs.", "body_md": "arXiv:2609.27411v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/when-labels-are-scarce-an-oscillatory-state-space-model-for-vibration-diagnosis", "canonical_source": "https://www.machinebrief.com/news/when-labels-are-scarce-an-oscillatory-state-space-model-for-8ckc", "published_at": "2026-09-24 04:00:00+00:00", "updated_at": "2026-09-24 05:00:27.592555+00:00", "lang": "en", "topics": ["machine-learning", "ai-research"], "entities": ["DualRes"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/when-labels-are-scarce-an-oscillatory-state-space-model-for-vibration-diagnosis", "markdown": "https://wpnews.pro/news/when-labels-are-scarce-an-oscillatory-state-space-model-for-vibration-diagnosis.md", "text": "https://wpnews.pro/news/when-labels-are-scarce-an-oscillatory-state-space-model-for-vibration-diagnosis.txt", "jsonld": "https://wpnews.pro/news/when-labels-are-scarce-an-oscillatory-state-space-model-for-vibration-diagnosis.jsonld"}}