Knowledge Graph Equipment Management for Diagnostic Data
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Solution Overview
Problem
Conventional systems lack the ability to effectively process and utilize vast amounts of data from diagnostic equipment to create 'smart' environments that can self-monitor, self-diagnose, and facilitate human-machine interactions, particularly in complex scenarios such as identifying the cause of unusual issues like unfamiliar odors in industrial settings.
Innovation Solution
An integrated monitoring and communications system using knowledge graph based explanatory equipment management, which leverages natural language processing and heterogeneous data to provide intuitive and explanatory assessments of equipment health and performance issues, enabling real-time monitoring and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data collection systems are used to gather information from diagnostic equipment, then large amounts of data can be collected, but the ability to provide meaningful analytics and insights is limited
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between raw diagnostic data and user queries. The knowledge graph processor transforms unstructured data from multiple sources into structured knowledge representations, enabling meaningful analytics without losing information. This intermediary structure allows the system to handle both large data volumes and provide deep analytical insights simultaneously.
Solution Approach 2:
The patent replaces traditional mechanical data processing systems with an AI-based knowledge graph processor. Instead of using conventional data storage and retrieval mechanisms, the system employs natural language processing, semantic understanding, and knowledge representation techniques to transform and analyze data, thereby enabling sophisticated analytics capabilities.
2Productivity
If conventional monitoring systems are used to track equipment data, then basic data storage and exchange can be achieved, but real-time processing and transformation of information for immediate impact on asset management is insufficient
Solution Approach 1:
The patent implements preliminary action by continuously pre-processing and structuring diagnostic data into knowledge graphs in advance. The knowledge graph processor maintains updated representations of equipment states, relationships, and contextual information ready for immediate querying. This allows the system to provide rapid responses to asset management questions without time-consuming data processing delays.
3Reliability
If traditional asset management systems are used to monitor equipment failures, then simple threshold-based predictions can be made, but the ability to diagnose complex or nuanced problems is limited
Solution Approach 1:
The patent applies dynamics by creating a flexible, adaptive knowledge graph structure that can evolve and accommodate complex diagnostic scenarios. The system dynamically adjusts its knowledge representations based on the specific problem being investigated, allowing it to handle everything from simple threshold failures to nuanced multi-causal problems. The knowledge graph processor can adapt its query and analysis approaches based on the complexity of the diagnostic challenge.
4Ease of operation
If conventional interfaces are used for operator interaction with diagnostic data, then basic data display can be provided, but intuitive inquiry and determination of problem causes is not enabled
Solution Approach 1:
The patent implements self-service by enabling operators to directly query the knowledge graph using natural language without requiring specialized training or complex interface navigation. The knowledge graph processor automatically interprets operator questions, retrieves relevant information from structured knowledge representations, and provides actionable insights. This allows operators to independently diagnose complex problems while maintaining simple, intuitive interaction.
Data Source
AI summary
A system for providing integrated monitoring and communications of diagnostic equipment is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive heterogeneous data from a plurality of machine and sensor equipment associated with performance of a system or product. The data access interface may also to receive a user inquiry pertaining to the system and product. The processor may generate a knowledge graph based on the data associated with the system or product, as well as convert the user inquiry into a knowledge graph query by: extracting entities from the user inquiry; extracting relations from the user inquiry to identify relationships between entities; expanding the user inquiry using the knowledge graph and the entities and relations; and translating the inquiry into knowledge graph triples. The processor may then identify relevant nodes and edges based on the knowledge graph query and the knowledge graph, and determine an answer to the user inquiry.