From Documents to Database: Failure Modes for Industrial Assets
- URL: http://arxiv.org/abs/2509.17834v1
- Date: Mon, 22 Sep 2025 14:23:50 GMT
- Title: From Documents to Database: Failure Modes for Industrial Assets
- Abstract summary: We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment.<n>Our system aggregates unstructured content across documents to generate an FMEA and stores it in a relational database.
- Score: 4.306143768014157
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment. Our system aggregates unstructured content across documents to generate an FMEA and stores it in a relational database. Leveraging this tool, the time required for creation of this knowledge-intensive content is reduced, outperforming traditional manual approaches. This demonstration showcases the potential of foundation models to facilitate the creation of specialized structured content for enterprise asset management systems.
Related papers
- From Reflection to Repair: A Scoping Review of Dataset Documentation Tools [10.124271544484634]
We present a systematic review supported by mixed-methods analysis of 59 dataset documentation publications.<n>Our analysis shows four persistent patterns in dataset documentation conceptualization that potentially impede adoption and standardization.<n>Building on these findings, we propose a shift in Responsible AI tool design toward institutional rather than individual solutions.
arXiv Detail & Related papers (2026-02-17T19:37:16Z) - LLM-Driven Ontology Construction for Enterprise Knowledge Graphs [0.0]
This paper introduces OntoEKG, a pipeline designed to accelerate the generation of domain-specific unstructured from enterprise data.<n>Our approach decomposes the modelling task into two distinct phases: an extraction module that identifies core classes and properties, and an entailment module that logically these elements into a hierarchy before serialising them into standard RDF.<n>Addressing the significant lack of comprehensive benchmarks for end-to-end construction, we adopt a new evaluation dataset derived from documents across the Data, Finance, and Logistics sectors.
arXiv Detail & Related papers (2026-02-01T15:13:30Z) - Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
arXiv Detail & Related papers (2025-10-17T02:33:16Z) - Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text [75.77648333476776]
This paper introduces an automated pipeline for extracting BPMN models from text.<n>A key contribution of this work is the introduction of a newly annotated dataset.<n>We augment the dataset with 15 newly annotated documents containing 32 parallel gateways for model training.
arXiv Detail & Related papers (2025-07-11T07:25:55Z) - DREAM: Document Reconstruction via End-to-end Autoregressive Model [53.51754520966657]
We present an innovative autoregressive model specifically designed for document reconstruction, referred to as Document Reconstruction via End-to-end Autoregressive Model (DREAM)<n>We establish a standardized definition of the document reconstruction task, and introduce a novel Document Similarity Metric (DSM) and DocRec1K dataset for assessing the performance of the task.
arXiv Detail & Related papers (2025-07-08T09:24:07Z) - FMEA Builder: Expert Guided Text Generation for Equipment Maintenance [0.009986418756990157]
We discuss the use of foundation models to generate structured documents related to critical assets.
A Failure Mode and Effects Analysis (FMEA) captures the composition of an asset or piece of equipment.
Our system uses large language models to enable fast and expert supervised generation of new FMEA documents.
arXiv Detail & Related papers (2024-11-07T13:11:03Z) - A Blueprint Architecture of Compound AI Systems for Enterprise [18.109450556443782]
We introduce a blueprint architecture for compound AI systems to operate in enterprise settings cost-effectively and feasibly.
Our proposed architecture aims for seamless integration with existing compute and data infrastructure, with stream'' serving as the key orchestration concept.
arXiv Detail & Related papers (2024-06-02T01:16:32Z) - LLM Based Multi-Agent Generation of Semi-structured Documents from
Semantic Templates in the Public Administration Domain [2.3999111269325266]
Large Language Models (LLMs) have enabled the creation of customized text output satisfying user requests.
We propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired structure.
arXiv Detail & Related papers (2024-02-21T13:54:53Z) - Learning to Extract Structured Entities Using Language Models [52.281701191329]
Recent advances in machine learning have significantly impacted the field of information extraction.
We reformulate the task to be entity-centric, enabling the use of diverse metrics.
We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
arXiv Detail & Related papers (2024-02-06T22:15:09Z) - Serving Deep Learning Model in Relational Databases [70.53282490832189]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks.
The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - Enterprise Model Library for Business-IT-Alignment [0.0]
This work is the reference architecture of a repository for models with function of reuse.
It includes the design of the data structure for filing, the processes for administration and possibilities for usage.
arXiv Detail & Related papers (2022-11-21T11:36:54Z) - CateCom: a practical data-centric approach to categorization of
computational models [77.34726150561087]
We present an effort aimed at organizing the landscape of physics-based and data-driven computational models.
We apply object-oriented design concepts and outline the foundations of an open-source collaborative framework.
arXiv Detail & Related papers (2021-09-28T02:59:40Z) - Automatic Construction of Enterprise Knowledge Base [6.6421796160706945]
We present an automatic knowledge base construction system from large scale enterprise documents with minimal efforts of human intervention.
This system is currently serving as part of a Microsoft 365 service.
arXiv Detail & Related papers (2021-06-29T04:29:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.