YOTOnet: Zero-Shot Cross-Domain Fault Diagnosis via Domain-Conditioned Mixture of Experts

TL;DR

YOTOnet is a novel zero-shot cross-domain fault diagnosis model for mechanical equipment that leverages foundation model principles to achieve robust generalization across diverse datasets.

Contribution

The paper introduces YOTOnet, combining physics-aware feature extraction and domain-conditioned experts, enabling effective zero-shot fault diagnosis without external meta-data.

Findings

YOTOnet outperforms state-of-the-art methods on five public datasets.

Test F1 score improves from 0.5339 to 0.705 when increasing training datasets from 1 to 4.

Clear scaling effect demonstrates robustness and generalization capability.

Abstract

Mechanical equipment forms the critical backbone of modern industrial production, yet domain shift severely limits the generalization of deep learning based fault diagnosis models across different equipment and operating conditions.Inspired by the success of foundation models in achieving zero-shotgeneralization, we propose YOTOnet (You Only Train Once), a novel architecture specifically designed for cross-domain fault diagnosis in mechanical equipment.YOTOnet comprises three core components: (1) a physics-aware Invariant Feature Distiller that extracts domain-agnostic representations using multi-scale dilated convolutions and FFT-based time-frequency fusion,(2) Domain-Conditioned Sparse Experts (DC-MoE) that adaptively route inputs to specialized processors via learned gating without external meta-data, and (3) a dual-head classification system with auxiliary supervision.Extensive validation on five public bearing datasets (CWRU, MFPT, XJTU,OTTAWA, HUST) through 30 cross-dataset protocols demonstrates the superiority of YOTOnet compared with other state-of-the-art methods. Critically, we observe a clear scaling effect-average test F1 improves from 0.5339(1 training dataset) to 0.705 (4 datasets), with a clear gain when moving from 3 to 4 datasets. These findings provide empirical evidence that foundation model principles can enable robust, train-once deployment for industrial fault diagnosis.