FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

FISHER, a foundation model for multi-modal industrial signal representation, addresses the M5 problem of data heterogeneity through a novel sub-band modeling approach that effectively manages variable sampling rates without resampling. Pre-trained via teacher-student self-distillation on external audio and music data, FISHER demonstrates superior performance against 24 state-of-the-art series encoders, achieving up to 16x smaller model sizes while maintaining high diagnostic accuracy. The establishment of the RMIS benchmark, which includes 19 datasets across four modalities, provides a robust framework for evaluating multi-modal industrial signal processing, making FISHER a significant advancement for practitioners in the field.