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 \begin{document}
 \maketitle
 
+\begin{abstract}
+Reliable bearing fault diagnosis is difficult when real fault data cannot be collected without risking machine damage. We study synthetic-to-real diagnosis under a strict inductive setting in which training uses labelled synthetic faults and healthy data from the target machine, while no target-fault samples, labelled or unlabelled, are available before evaluation. This avoids the transductive use of fault examples from the test domain adopted in many domain-adaptation studies and preserves a clear separation between adaptation and testing. We propose a healthy-baseline residual framework that subtracts the mean healthy spectrum of each domain to suppress shared background structure and emphasize transferable fault signatures. The residual is combined with the log-magnitude spectrum and processed by a cosine-prototype classifier with conditional domain adaptation. Across multiple bearing benchmarks, the framework recovered every real fault class that remained unseen during training and, on most benchmarks, outperformed established adaptation and physics-guided baselines while remaining competitive on the rest. Ablation experiments attributed the main improvement to the residual representation and its interaction with the angular classifier rather than to the adaptation procedure alone. These findings show that healthy-only target data can support cross-domain bearing diagnosis when the framework isolates physically meaningful fault structure without relying on target-fault observations.
+\end{abstract}
+
+
 \section{Setting}
 We train a fault-type classifier using only (i) \emph{synthetic} fault signals and
 (ii) \emph{real healthy} recordings from the target machine; no real fault labels are