FMEA Knowledge Graph: Integrating Failure Analysis with AI for Intelligent Manufacturing

Knowledge graphs are a core technology of cognitive AI and a key factor for enterprises to maintain a sustainable competitive edge. Product quality and manufacturing reliability are the lifelines of intelligent manufacturing, and Failure Mode and Effects Analysis (FMEA) is a crucial method for quality and reliability engineering. Drawing on more than ten years of AI practice in semiconductor, high‑end medical device, automotive, new energy and carbon‑neutrality fields, the author deeply analyzes the FMEA knowledge graph.

Daguan Data is a national high‑tech enterprise specializing in natural language processing, knowledge graphs, and RPA. It has been recognized as a "Specialized, Refined, Distinct, New" enterprise and a "Technology Giant" by the Ministry of Industry and Information Technology, ranked among China’s top‑50 AI companies, and holds numerous patents, software copyrights, and academic publications.

The background of the FMEA knowledge graph is illustrated by the extreme complexity and precision requirements of chip manufacturing, especially photolithography, where a single wafer may undergo 20‑30 lithography steps, accounting for 40‑60% of the total process time and one‑third of the cost. Similar reliability challenges exist in smartphone chip production and other high‑precision manufacturing processes, where each process step must achieve reliability above 99.999% to ensure acceptable overall yield.

Quality and reliability are the lifelines of manufacturing. Case studies such as AIAG’s automotive industry collaboration and TSMC’s quality‑improvement initiatives show that systematic failure analysis and preventive actions can generate billions of yuan in value, highlighting the strategic importance of knowledge‑driven quality control.

FMEA, originating from NASA’s rocket‑launch failure analysis, systematically identifies potential failure modes, their effects, and causes during product and process design. It has become a core practice in automotive lean manufacturing and is now widely applied in semiconductor, new energy, medical equipment, aerospace, and other high‑end manufacturing sectors.

Numerous industry standards (automotive, aerospace, nuclear, bearing manufacturing, etc.) have been established to ensure the effective implementation of FMEA across the product lifecycle, from requirement analysis to after‑sales service.

Traditional FMEA does not fully exploit AI. With the rapid development of deep learning, knowledge graphs have become industrial‑scale, enabling the combination of AI and FMEA to build an intelligent FMEA knowledge graph that serves as a “vaccine” for product quality and process reliability.

Daguan’s Yuanhai Knowledge Graph platform provides strong data‑management capabilities (supporting structured and unstructured data, multiple data sources, seamless RPA integration), multi‑graph management, powerful graph construction and editing tools, advanced algorithms (deep learning, graph computing), fine‑grained permission control, and customizable development services.

The construction of the FMEA knowledge graph follows the "human‑machine‑material‑method‑environment‑measurement" dimensions, linking products, equipment, R&D, production, management, and after‑sales data. It also integrates BOM, design changes, supplier, customer, and market information, as well as research papers, patents, and competitive intelligence, to create a comprehensive, multi‑dimensional knowledge base.

Applications include semantic‑driven failure attribution (inputting a fault description to automatically retrieve the most relevant failure causes and remediation), assisted FMEA creation (auto‑generating DFMEA, PFMEA, MFMEA from the knowledge graph, drastically shortening the preparation cycle), version management and intelligent comparison (document‑level and item‑level diffing), and new failure‑mode discovery through knowledge reasoning on reports, papers, and news.

Because manufacturing knowledge constantly evolves, the platform is designed to be operable and manageable, offering functions such as model, permission, algorithm, and vocabulary management, as well as graph design, editing, CRUD, visualization, and lifecycle governance.

In summary, Daguan Data’s Yuanhai Knowledge Graph platform empowers high‑end manufacturing industries—including aerospace, automotive, semiconductor, marine, power, energy, medical devices, and defense—to improve product quality and production reliability. For deeper insights into knowledge‑graph theory and practice, refer to Wang Wenguang’s book "Knowledge Graph: Cognitive AI Theory and Practice".