A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery
Integrating multi-level information, from physical models through data-driven diagnostics to natural language reasoning, into verifiable decision chains is a growing need in intelligent manufacturing. In bearing fault diagnosis, taken here as a representative testbed, the standard output is a class label and a confidence score derived from the classifier's own distribution, offering limited means of comparison against independent physical knowledge. Meanwhile, language models increasingly used for maintenance communication may introduce unsupported content. This work addresses both limitations from the output side. The proposed Diagnostic Evidence Network (DENet) is an encoder-agnostic mu...
| Authors | Yuntong Chen, Jianyu Liu, Yingqi Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 July 2026 |
| Last updated at source | 24 September 2026 |
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