FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation
Pingyi Fan, Anbai Jiang, Shuwei Zhang, Zhiqiang Lv, Bing Han, Xinhu Zheng, Wenrui Liang, Junjie Li, Wei-Qiang Zhang, Yanmin Qian, Xie Chen, Cheng Lu and Jia Liu
随着SCADA系统的快速部署，如何有效分析工业信号并检测异常状态成为工业界的迫切需求。由于这些信号具有显著的异质性（我们将其总结为M5问题），先前的研究仅关注小的子问题并使用专用模型，未能利用模态间的协同效应和强大的缩放定律。然而，我们认为由于内在相似性，M5信号可以用统一方式建模。因此，我们提出了FISHER——一个用于多模态工业信号综合表征的基础模型。为支持任意采样率，FISHER将采样率的增量视为子带信息的拼接。具体而言，FISHER以STFT子带作为建模单元，并采用师生自监督学习框架进行预训练。我们还开发了RMIS基准，用于评估M5工业信号在多个健康管理任务中的表征能力。与顶级自监督学习模型相比，FISHER展现出全面且卓越的能力，综合性能提升最高达5.03%。
With the rapid deployment of SCADA systems, how to effectively analyze industrial signals and detect abnormal states is an urgent need for the industry. Due to the significant heterogeneity of these signals, which we summarize as the M5 problem, previous works only focus on small sub-problems and employ specialized models, failing to utilize the synergies between modalities and the powerful scaling law. However, we argue that the M5 signals can be modeled in a unified manner due to the intrinsic...