当标签稀缺时：用于振动诊断的振荡状态空间模型
When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis
- Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对振动诊断中标签稀缺和边缘设备计算受限的问题，提出紧凑振荡状态空间模型DualRes，融合振动互补频谱视图，以选择性振荡记忆处理时间模式，仅含39,528参数，在齿轮箱基准上多数标签预算下达到最先进性能，显著提升宏F1并减少存储。
AI中文摘要：
基于振动的机器故障诊断需要在稀缺的带标签故障录音中学习，同时满足边缘设备进行本地推理的计算约束。我们引入了DualRes，一种紧凑的振荡状态空间模型，它结合了振动的两个互补频谱视图，捕捉快速变化和精细频率结构。时间对齐的视图由选择性振荡记忆处理，该记忆学习保留时间模式的时间长度。编码器包含39,528个参数。我们在六个轴承数据集和一个齿轮箱基准上评估了监督学习，并附加了一个齿轮箱试点。记录级别的划分和标签持续时间的明确说明将数据效率与对相关样本的重复暴露区分开来。在主要的齿轮箱基准上，DualRes在七个标签预算中的六个上，在九种评估方法中达到了最先进的性能。每类约有六秒的标签数据，它比次强比较器的宏F1提高了16.1个百分点。在同一基准上，与匹配硬件和运行时条件下的选择性状态空间基线相比，DualRes实现了1.44倍的记录级加速和24.8倍的检查点存储减少。轴承结果揭示了任务相关的权衡。这些发现支持振荡记忆作为在有限标签暴露下进行振动诊断的一种紧凑方法。
英文摘要：
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.
