VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
Haiteng Wang, Jingheng Yan, Xiaokang Wang, Lei Ren
Why It Matters
What makes this one worth your time
This work is significant for AI researchers and engineers focused on industrial applications, as it offers a novel approach to integrating diverse data modalities, potentially improving the reliability and safety of industrial systems.
VLT is a multimodal model that enhances industrial intelligence by integrating time-series, visual, and textual data.
Summary
The paper introduces VLT, a multimodal foundation model designed to integrate time-series data, frequency-spectrum visual representations, and textual knowledge for industrial intelligence applications. It employs a Time-aware Mixture-of-Experts to capture temporal dynamics and a Frequency-Text Augmented Learner for joint spectral and semantic modeling. A time-centric gradient alignment mechanism is also proposed to address cross-modal optimization conflicts. The model demonstrates improved performance over state-of-the-art methods in various challenging scenarios.
Key contributions
- Development of a Time-aware Mixture-of-Experts for capturing temporal dynamics.
- Introduction of a Frequency-Text Augmented Learner for joint spectral and semantic modeling.
- Proposal of a time-centric gradient alignment mechanism for cross-modal optimization.
Notable insights
- Using frequency spectrum as a visual bridge to connect continuous temporal signals with discrete semantics.
- Time-centric gradient alignment mechanism to mitigate cross-modal optimization conflicts.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.14510v1 Announce Type: new Abstract: Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines. However, existing approaches are typically limited to single-modality modeling, which restricts their generalization in complex scenarios. Although recent advances in large language models (LLMs) provide new opportunities for multimodal learning, bridging continuous time-series signals and discrete textual semantics remains an open challenge. To this end, we propose VLT, a multimodal foundation model that jointly models time-series, frequency-spectrum visual representations, and textual knowledge. A key insight is to utilize the frequency spectrum as a visual bridge to connect continuous temporal signals with discrete semantics. Specifically, a Time-aware Mixture-of-Experts (Time-MoE) is designed to capture heterogeneous temporal dynamics, while a Frequency-Text Augmented Learner enables joint modeling of spectral and semantic features within a shared representation space. Furthermore, a time-centric gradient alignment mechanism is introduced to mitigate cross-modal optimization conflicts via gradient normalization and reliability-aware dynamic reweighting. Extensive experiments on multiple industrial datasets demonstrate that VLT outperforms state-of-the-art methods, achieving superior robustness and generalization under few-shot, noisy, and incomplete-modality settings.