# A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery

arXiv: https://arxiv.org/abs/2607.22797 · HTML: https://mlsanity.com/paper/2607.22797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models](https://mlsanity.com/paper/2502.15481/) (2025)

## Abstract (arXiv)

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 multi-task framework that extends the output to a structured evidence record: the classification, a predicted characteristic frequency comparable against the theoretical value determined by bearing geometry and shaft speed, and a temporal localization of transient impulses inspectable on the raw waveform. Across four encoders and three public datasets, this evidence incurs no statistically significant accuracy cost, with a frequency error of about 6 Hz on 1,024-point segments. The deviation between predicted and theoretical frequency constitutes a label-free, inference-time validation signal. It detects misclassifications with AUROC of 0.970 and 0.871, and retains separation within the high-confidence subset. Finally, a QLoRA-adapted language model renders DENet's evidence into traceable maintenance reports without contributing diagnostic decisions, reducing unsupported-claim rates from 10-12% to 2% with no fabricated quantities observed.