VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence

• URL: http://arxiv.org/abs/2607.14510v1
• Date: Thu, 16 Jul 2026 02:53:54 GMT
• Title: VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
• Abstract summary: Industrial time series serve as the foundation for Prognostics and Health Management (PHM)<n>We propose VLT, a multimodal foundation model that jointly models time-series, frequency-spectrum visual representations, and textual knowledge.<n>VLT outperforms state-of-the-art methods, achieving superior robustness and generalization under few-shot, noisy, and incomplete-modality settings.
• Score: 8.44621217686077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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