VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis

2024-09-03

arXiv.org

AI Summary

To address the lack of domain-specific knowledge in large multimodal models (LMMs) for industrial vibration signal analysis, this paper introduces the first multimodal large-model pipeline tailored for vibration analytics. Methodologically: (1) we design an expert-rule-guided signal-text triplet construction mechanism to enhance data domain fidelity; (2) we propose the first CLIP–large language model (LLM) joint low-rank adaptation (LoRA) framework to enable cross-modal alignment between vibration time-series signals and natural language; and (3) we integrate instruction tuning with multimodal alignment training. Experiments demonstrate significant improvements in parameter identification accuracy and fault diagnosis relevance, enabling end-to-end, interpretable vibration analysis in real-world industrial settings. The core contribution lies in deeply embedding domain expertise into the multimodal modeling paradigm—thereby bridging a critical gap in LMMs’ understanding of industrial time-series signals.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Large multimodal foundation models have been extensively utilized for image recognition tasks guided by instructions, yet there remains a scarcity of domain expertise in industrial vibration signal analysis. This paper presents a pipeline named VSLLaVA that leverages a large language model to integrate expert knowledge for identification of signal parameters and diagnosis of faults. Within this pipeline, we first introduce an expert rule-assisted signal generator. The generator merges signal provided by vibration analysis experts with domain-specific parameter identification and fault diagnosis question-answer pairs to build signal-question-answer triplets. Then we use these triplets to apply low-rank adaptation methods for fine-tuning the linear layers of the Contrastive Language-Image Pretraining (CLIP) and large language model, injecting multimodal signal processing knowledge. Finally, the fine-tuned model is assessed through the combined efforts of large language model and expert rules to evaluate answer accuracy and relevance, which showcases enhanced performance in identifying, analyzing various signal parameters, and diagnosing faults. These enhancements indicate the potential of this pipeline to build a foundational model for future industrial signal analysis and monitoring.

Problem

• Addresses lack of domain knowledge in industrial vibration analysis
• Develops specialized multimodal model for signal identification tasks
• Enhances fault-related signal classification and parameter analysis

Innovation

• Expert knowledge-guided instruction tuning
• Two-stage learning with LoRA fine-tuning
• Dual-mode evaluation framework with LLM referee