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Traditional methods relying on artificial rules and static knowledge bases struggle to effectively integrate multimodal information such as text, images, and sensors. This paper proposes a decision support system for equipment fault diagnosis based on large language models (LLMs), with an unstructured industrial knowledge graph (UIKG) as the core knowledge carrier. We construct a dynamic UIKG driven by multimodal data, abandoning static manual construction modes. The system employs a domain\u2010adaptive LLM combined with contrastive learning and low\u2010rank fine\u2010tuning to enhance professional terminology understanding and contextual reasoning. A cloud\u2010edge collaborative architecture balances real\u2010time response and computational constraints through lightweight deployment and dynamic knowledge subgraph transmission. Temporal weight update and distributed graph incremental mechanisms ensure dynamic knowledge evolution. Experiments on port cranes and wind turbines show that the system reduces the missed detection rate to 1.5% and maintenance costs by over 20%, while supporting rapid cross\u2010device migration. The core novelties of this work are threefold: (1) proposing a domain\u2010adaptive UIKG construction method driven by multimodal data, breaking the static manual construction mode of traditional industrial knowledge graphs; (2) designing a cloud\u2010edge collaborative dynamic UIKG update and reasoning framework, balancing the real\u2010time performance of edge fault diagnosis and the depth of cloud knowledge analysis; and (3) integrating LoRA fine\u2010tuning and contrastive learning to realize cross\u2010modal semantic alignment of industrial text, image, and sensor data, solving the problem of semantic discretization of unstructured industrial data. Future research will focus on federated learning frameworks and multimodal generation technologies to further optimize knowledge sharing mechanisms and adaptive reasoning capabilities.<\/jats:p>","DOI":"10.1155\/int\/4611626","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:29:53Z","timestamp":1778322593000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decision Support System for Equipment Fault Diagnosis Based on Large Language Model: Dynamic Construction of Unstructured Industrial Knowledge Graph for Industrial Equipment"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8062-2087","authenticated-orcid":false,"given":"Yu","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"crossref","unstructured":"ChaiY.andHouJ. 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