arXiv cs.AI· Xinyu Zou, Jian Ma, Liang Ma, Chen Lu, Tao Peng, Yingjie Li·· 1 天前AI 评分27
TFC-MMFD：面向多元素异构场景的旋转机械故障诊断时频条件多模态融合基础模型
A Time-Frequency-Condition Multimodal Fusion Foundation Model for Universal Rotating-Machinery Fault Diagnosis under Multi-Element Heterogeneous Scenarios
AI 导读
TFC-MMFD 是一个基于 LLaMA 主干的时频条件多模态融合基础模型，将统计指标、时频图像、振动序列与机器条件文本统一 token 化后做故障诊断。在十个公开轴承数据集的四项递进任务上，它达到 99.40% 准确率和 99.16% macro-F1，多数插值未见工况下无需适配仍保持 80% 以上准确率，每类仅五个标注样本时在新机器上从 20.26% 提升至 95.63%。
正文 · 原文
Abstract:Intelligent fault diagnosis models are typically developed for a single machine under a specific set of operating conditions, such that each new machine or condition requires retraining from scratch. This one-machine-one-model paradigm scales poorly. Although transfer learning and domain generalization can reduce the labeling burden, they primarily seek domain-invariant commonality, potentially discarding machine-specific characteristics that are critical for diagnosis. This paper proposes TFC-MMFD, a time-frequency-condition multimodal fusion foundation model for rotating-machinery fault diagnosis under multi-element heterogeneous scenarios. Statistical indicators, time-frequency images, vibration sequences, and textual machine and condition descriptions are tokenized into a unified language-like space, allowing inputs of different physical origin to be represented as a single sequence. A LLaMA backbone, together with cross-modal bidirectional attention and a Q-Former, then distills this sequence into fault-mode queries. Diagnosis is performed by matching these queries against natural-language fault-mode anchors rather than fixed class indices, while regression-adversarial condition disentanglement and condition-conditioned masked pre-training improve robustness to changing conditions. Across four progressive tasks on ten public bearing datasets spanning diverse bearing types, speeds, and loads, TFC-MMFD attains 99.40% accuracy and 99.16% macro-F1, maintains over 80% accuracy on most interpolated unseen conditions without adaptation, and improves from 20.26% to 95.63% accuracy on an unseen machine with only five labeled samples per class. It outperforms the strongest of eight domain-generalization baselines by 10.57 percentage points and compares favorably with a recent bearing foundation model while using less than half of its fine-tuning data.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15051 [cs.CL] |
| (or arXiv:2611.15051v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15051 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yingjie Li [view email]
[v1]
Wed, 11 Nov 2026 05:25:10 UTC (3,521 KB)
来源：arXiv cs.AI · arxiv.org