Automated Knowledge Graph Learning in Industrial Processes
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingGraph LearningKnowledge GraphsTime SeriesSimilar Papers 제목 키워드 기반
Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes
Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages. Effective monitoring and timely …
Time Series Anomaly DetectionGraph LearningRoot-KGD: A Novel Framework for Root Cause Diagnosis Based on Knowledge Graph and Industrial Data
With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnos…
Fault DiagnosisKANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the n…
Graph AttentionGraph structure learningRepresentation LearningOn Event-Driven Knowledge Graph Completion in Digital Factories
Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and en…
Knowledge Graph CompletionKnowledge GraphsAutomated Extraction and Creation of FBS Design Reasoning Knowledge Graphs from Structured Data in Product Catalogues Lacking Contextual Information
Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological m…
Knowledge Graphs