Regional Sensor Data Determination via Segmented Array Models
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Solution Overview
Problem
Existing sensor systems face challenges in determining accurate regional sensor data due to conductivity failures and varying measurement resolution needs, which can lead to reduced accuracy and increased costs for hardware modifications or repairs.
Innovation Solution
The system groups sensors into regions with unique electrical connections and applies region-specific models to generate regional sensor values, allowing for continuous operation even with failed sensors and adjustable measurement resolution based on application requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are grouped into regions with unique electrical connections, then reliability is improved by maintaining operation with failed sensors, but device complexity increases due to region-specific models and data processing
Solution Approach 1:
The sensor array is divided into multiple sensor regions, where each region has unique electrical connections to the controller. This segmentation allows the system to continue operating with reduced functionality when individual sensors or small groups of sensors fail, as each region can be independently managed. The controller receives sensor data from multiple regions and can compensate for failures in specific regions by using data from other regions.
Solution Approach 2:
The system changes the parameter of electrical connectivity by providing unique electrical connections for each sensor region rather than sharing connections. This parameter change enables the controller to identify and compensate for regional sensor failures, improving overall system reliability. The region-specific approach allows dynamic adjustment of data processing based on the operational status of different regions.
2Measurement precision
If measurement resolution is increased to improve accuracy, then measurement precision is improved, but manufacturing cost increases due to additional sensors and electrical connections
Solution Approach 1:
The sensor array is segmented into multiple regions with unique electrical connections, allowing the system to achieve high measurement precision in each region without requiring every sensor in the entire array to be actively used at full resolution. The controller can process data from multiple regions to reconstruct high-resolution sensor data, effectively distributing the precision requirement across multiple lower-cost regional measurements.
Solution Approach 2:
The controller is designed to perform multiple functions: it processes sensor data from all operational regions, identifies regional failures, compensates for missing data, and reconstructs overall sensor readings. This multi-functionality allows the system to maintain high measurement precision using data from multiple regions rather than requiring every individual sensor to operate at maximum resolution, thereby reducing manufacturing costs.
3Measurement precision
If hardware modifications are made to repair failed sensors, then measurement precision is maintained, but loss of time and productivity occur due to system downtime
Solution Approach 1:
The system implements beforehand cushioning by providing unique electrical connections for each sensor region and implementing failure detection and compensation mechanisms. When sensor failures occur, the controller can immediately identify the affected regions and compensate for the missing data using information from other regions, cushioning against the impact of failures and maintaining measurement precision without requiring immediate hardware repairs or system shutdowns.
4Reliability
If region-specific models are applied to compensate for failed sensors, then reliability is improved, but computational complexity increases
Solution Approach 1:
The computational complexity is managed through segmentation by applying region-specific models only to the regions that have experienced sensor failures, rather than processing all sensor data through complex models. The controller can identify which regions are operational and which have failures, then apply compensation algorithms selectively to the affected regions, reducing overall computational burden while maintaining reliability.
Data Source
AI summary
A system, method and computer program product for determining regional sensor data. Sensor readings are obtained from a corresponding plurality of sensors. The sensors are grouped into a plurality of sensor regions. Each sensor region includes at least one region-associated sensor from the plurality of sensors. A plurality of regional sensor values are determined for the plurality of sensor regions by, for each sensor region: identifying a region-specific model, the region-specific model specifying how the at least one regional sensor value for that sensor region is to be calculated; and generating the at least one regional sensor value for that sensor region by applying the region-specific model to the sensor readings obtained from the at least one region-associated sensor corresponding to that sensor region. The regional sensor values can account for sensor and conductivity failures and allow the measurement resolution of the sensing unit to be adjusted.