Virtual Sensor Data Reconstruction via Topology-Based Candidate Selection
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Solution Overview
Problem
In complex systems with multiple sensors, reconstructing faulty sensor data reliably and efficiently is challenging due to the high computational requirements and human intervention needed in existing approaches, which complicates the detection of critical operating states and system control.
Innovation Solution
A monitoring device with a receiving and evaluation unit that identifies sensor candidates based on structural and functional relationships, calculates similarity values, and generates virtual sensor data using the data from the most similar functioning sensors, reducing computational load and reliance on human knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a statistics-based approach is used to identify relationships between sensor data, then the reliability of sensor data reconstruction is improved, but the computing power requirement and hardware complexity increase significantly
Solution Approach 1:
The patent segments the sensor set into groups based on spatial proximity and functional relationships. Instead of calculating statistical relationships for all sensor pairs (which would be O(n²) complexity), the system only evaluates sensors within localized groups, dramatically reducing computational requirements while maintaining reconstruction reliability through structurally-informed candidate selection.
Solution Approach 2:
The patent performs preliminary structural analysis to identify candidate sensors before fault occurrence. By pre-establishing spatial and functional relationships between sensors during normal operation, the system creates a ready-made candidate list that can be quickly consulted when faults occur, avoiding the need for complex real-time statistical calculations.
2Reliability
If a knowledge-based approach is used to identify related sensor data, then the reliability of sensor data reconstruction is improved, but the complexity of system analysis and human intervention increase
Solution Approach 1:
The patent enables the monitoring system to automatically identify candidate sensors through structural analysis of the monitored object's geometry and sensor arrangements. The system self-configures by analyzing spatial coordinates and functional relationships without requiring external expert knowledge or manual knowledge base creation, thereby maintaining high reliability through objective structural criteria while eliminating human intervention complexity.
Solution Approach 2:
The patent replaces the manual knowledge-based approach (requiring expert analysis and knowledge base creation) with an automated structural analysis mechanism. By using mathematical models of spatial relationships and functional dependencies, the system objectively identifies candidate sensors without human intervention, substituting expert judgment with algorithmic structural analysis.
3Reliability
If all sensor pairs are evaluated for statistical relationships, then the completeness of candidate identification is improved, but the computing time and processing power increase
Solution Approach 1:
The patent applies local quality by evaluating statistical relationships only for sensor pairs that are spatially proximate or functionally related, rather than all possible pairs. This localized evaluation focuses computational resources on the most relevant sensor combinations, maintaining identification completeness for fault reconstruction while dramatically improving processing speed by eliminating evaluations of unrelated sensor pairs.
4Measurement precision
If detailed system analysis is performed to create a knowledge base, then the accuracy of sensor candidate identification is improved, but the time and resources required for system setup increase
Solution Approach 1:
The patent performs preliminary structural analysis during system initialization to establish spatial and functional relationships between sensors. By pre-computing candidate sensor lists based on the monitored object's geometry and sensor arrangements before operation begins, the system achieves high identification accuracy without requiring detailed manual system analysis during setup, thereby reducing initial configuration time and resources.
Data Source
Figure 1~3
AI summary
The invention relates to the monitoring of a technical system by means of sensor data. Upon failure of a sensor, virtual sensor data is created for the failed sensor on the basis of the remaining functional sensors. The sensors for calculation of the virtual sensor data are selected in two stages. In a first step, possible candidates of sensors are determined on the basis of a scientific approach and the topology of the system. In a second step, a mathematical relationship between the sensor data of a faulty sensor and the possible candidates of sensors is calculated for the calculation of the virtual sensor data. In this way, those sensors forming a suitable basis for the calculation of the virtual sensor data can be identified.