文献类型：期刊文献

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

作者：Wang, Zesen[1,2]; Wu, Zihao[1]; Hu, Yue[1]; Gao, Yang[1]; Xuan, Fuzhen[1]

机构：[1] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China; [2] Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260285365)

语种：英文

外文关键词：Deep learning - Statistical tests

摘要：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-shot generalization, 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 metadata, 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. Copyright ? 2026, The Authors. All rights reserved.