Context Information Generation for Sensor-Limited Device Diagnosis
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Solution Overview
Problem
Current automation systems face challenges in generating accurate diagnostic information due to separated and unlinked information contexts, lack of semantic interoperability, and absence of required contextual data, particularly in industrial automation devices where real-time sensor data is not available.
Innovation Solution
A method is developed to generate estimated context-dependent information by correlating data elements from various sources using semantic rule engines, based on known values and experiences, to create new contexts that assist users in diagnosing device issues without relying on direct sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic rule engines are used to generate estimated context-dependent information from available data sources, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
A semantic rule engine is introduced as an intermediary component that bridges the gap between available sensor data and required diagnostic information. The rule engine processes and correlates data from multiple sources (device sensors, environmental sensors, historical data) to generate estimated context-dependent information, thereby improving diagnostic accuracy without requiring direct access to all possible data sources.
Solution Approach 2:
The system creates virtual representations of missing or unavailable sensor data by generating estimated context-dependent information based on correlations with available data. Instead of physically installing sensors everywhere, the system copies the functional capability of missing sensors through computational estimation using semantic rules that model expected relationships between different parameters.
2Loss of information
If context data is collected from multiple data sources and correlated, then information completeness is improved, but data processing time increases
Solution Approach 1:
Context data from multiple sources is pre-collected, pre-processed, and pre-correlated during periods when diagnostic analysis is not urgently needed. The semantic rule engine establishes and stores correlation relationships between different data sources in advance, so that when diagnostic information is required, the system can quickly retrieve and apply pre-established relationships rather than performing complex correlations in real-time.
3Adaptability or versatility
If estimated context-dependent information is generated without real sensor data, then system adaptability is improved, but measurement reliability decreases
Solution Approach 1:
The system applies different quality levels to different types of diagnostic information based on local conditions. When real sensor data is available, it is used directly with high reliability. When sensor data is unavailable, estimated context-dependent information is generated with appropriate confidence levels. The system adapts the quality and source of information locally depending on data availability, maintaining overall system reliability while improving adaptability to various operational scenarios.
Data Source
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AI summary
The invention relates to a method for generating context-dependent information to inform a user about the state of a technical system, wherein the context-dependent information of the technical system is provided by various data sources. To obtain information about a new context of the technical system without actual data sources, it is proposed that estimated context-dependent information of a new context be generated and provided to the user by correlating data elements from the various data sources, without knowledge of the actual values of the new context, but based on known and experience-based values.