Automatic Sensor Identification via Static and Dynamic Vector Correlation
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Solution Overview
Problem
Existing sensor systems require manual correlation of network identifiers with sensor module positions, which is inefficient and prone to errors, especially in dynamic environments where sensor orientations and positions change frequently.
Innovation Solution
A method that automatically maps network identifiers to sensor modules by correlating static and dynamic action vectors measured by sensor modules, using a two-stage process to determine unique orientations and positions, allowing for accurate identification even in changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correlation of network identifiers with sensor module positions is used, then system complexity is reduced, but identification accuracy and efficiency deteriorate
Solution Approach 1:
The system performs automatic identification of sensor modules through self-correlation of static and dynamic action vectors without requiring external manual intervention. The sensor modules autonomously provide their own identification data through the correlation process, eliminating the need for manual mapping while achieving accurate position and orientation identification
Solution Approach 2:
The system performs preliminary correlation of static action vectors before dynamic operation to establish initial sensor module identifications. This preliminary action creates a baseline mapping that can be refined during dynamic states, improving overall identification accuracy without adding complexity during critical operational phases
2Productivity
If automatic identification through vector correlation is implemented, then identification efficiency is improved, but measurement and detection difficulty increases
Solution Approach 1:
The identification process is segmented into distinct phases: static action vector correlation for initial identification, and dynamic action vector correlation for refinement. This segmentation breaks down the complex measurement task into manageable stages, improving efficiency while controlling complexity through structured progression
Solution Approach 2:
Action vectors serve as intermediary elements that bridge the gap between sensor module physical states and their network identifiers. By correlating these intermediate vector representations, the system efficiently matches sensors to positions without requiring direct complex measurement of each sensor's absolute state
3Adaptability or versatility
If sensor modules are mounted at various positions with different orientations, then system adaptability is improved, but identification reliability deteriorates due to increased complexity
Solution Approach 1:
The system exploits the asymmetric orientations of sensor modules as distinctive identifiers. Each sensor's unique orientation creates a characteristic static and dynamic action vector pattern that serves as its fingerprint, allowing reliable identification despite the variety of positions and orientations. The asymmetry in sensor configurations becomes the basis for differentiation rather than a source of confusion
4Reliability
If manual mapping is used in dynamic environments, then system complexity is reduced, but error rate increases
Solution Approach 1:
The automatic identification system operates continuously during both static and dynamic states, maintaining accurate sensor mappings without interruption. The correlation process runs continuously as sensors move and operate, ensuring mappings remain current without requiring periodic manual updates, thereby eliminating time loss while maintaining high reliability
Data Source
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AI summary
Disclosed is a method for mapping network identifiers to a set of sensor modules that measure a three-dimensional action vector and that are sensitive to orientation in three-dimensional space. Each sensor module is mounted at a different position on a machine such that the orientation of each sensor module is different. The method includes one or two stages. In the first stage, the machine is placed in a stationary state, and measurements of a static action vector from a sensor module identified by a network identifier are correlated with expected measurements from a sensor module having a corresponding orientation and corresponding position. In the second stage, the machine is placed in a dynamic state, and measurements of a dynamic action vector from a sensor module identified by a network identifier are correlated with expected measurements from a sensor module having a corresponding orientation and corresponding position.