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Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation
- Condition monitoring (CM) is essential in ensuring reliability and efficiency in the process industry.
- Computerized maintenance systems can detect and classify faults, but fault severity estimation and maintenance decisions often rely on human expert analysis.
- Automated systems currently exhibit uncertainty and high false alarm rates, leading to increased workload and reduced efficiency.
- A framework called MindRAG integrates large language model (LLM)-based reasoning agents with CM workflows.
- The goal is to reduce false alarms, enhance fault severity estimation, improve decision support, and provide explainable interfaces.
- MindRAG combines multimodal retrieval-augmented generation (RAG) with vector store structures designed for CM data.
- Annotations and maintenance work orders are used as surrogates for labels in training predictive models on noisy real-world datasets.
- Key contributions include structuring CM data into a multimodal vector store, developing tailored RAG techniques, creating practical reasoning agents, and presenting an experimental framework for evaluation.
- Preliminary results suggest that MindRAG offers meaningful decision support for better alarm management and enhanced interpretability of CM systems.
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