When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis

2026-09-23

AI Summary

针对振动诊断中标签稀缺的问题，提出了一种名为DualRes的紧凑振荡状态空间模型，通过结合两种互补的频谱视图来捕捉快速变化和精细频率结构，提高了在有限标签下的诊断性能。

Abstract

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.

Problem

• vibration diagnosis
• scarce labelled data
• edge devices
• computational constraints

Innovation

• DualRes
• Oscillatory State-Space Model
• Vibration Diagnosis
• Scarce Labelled Data
• Selective Oscillatory Memory