Infrastructure Monitoring System with Adaptive Data Filtering
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Solution Overview
Problem
Monitoring and controlling the performance of unique infrastructure structures like bridges in real-time is challenging due to varying operating parameters and unknown factors such as material deterioration and unexpected disturbances, which affects their safety and operational efficiency.
Innovation Solution
A three-tier architecture system that processes sensor data locally on the structure, filters and reduces it, and then transmits only the most valuable information to a remote central unit for analysis and control, enabling real-time monitoring, control, and adaptive learning across a network of bridges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time monitoring data is collected and transmitted from all sensors on a bridge, then the completeness and accuracy of performance information is improved, but the data transmission bandwidth and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the most valuable and relevant information from the raw sensor data at the local level before transmission. Local control units process sensor measurements and selectively transmit only critical performance indicators and anomaly detections to the central unit, rather than transmitting all raw sensor data. This extraction principle reduces data transmission bandwidth while preserving essential performance information.
Solution Approach 2:
The monitoring system is segmented into multiple hierarchical levels: sensor modules distributed across the bridge structure, local control units at intermediate levels, and a central unit. Each segment processes data locally and transmits only necessary information upward, dividing the data management burden and reducing overall transmission requirements while maintaining comprehensive monitoring coverage.
2Measurement precision
If comprehensive sensor data is transmitted and stored centrally, then the accuracy of performance analysis is improved, but the system complexity and cost increase
Solution Approach 1:
The system divides complexity across multiple hierarchical levels with specialized functions at each level. Sensor modules handle data acquisition, local control units perform initial processing and filtering, and the central unit conducts comprehensive analysis. This segmentation distributes system complexity rather than concentrating it all at the central unit, making the overall system more manageable while maintaining analysis accuracy.
Solution Approach 2:
Data processing and filtering actions are performed preliminarily at local control units before data reaches the central unit. This preliminary action reduces the volume and complexity of data requiring central processing, lowering system complexity while preserving the precision needed for accurate performance analysis through selective data transmission.
3Productivity
If local processing is implemented at sensor modules, then the data transmission efficiency is improved, but the device complexity at each sensor node increases
Solution Approach 1:
Sensor modules perform preliminary data processing and filtering actions locally before transmission. By preprocessing data at the source, the system improves transmission efficiency by sending only relevant information. The complexity added to individual sensor modules is minimal compared to the overall system benefits of reduced transmission bandwidth and faster response times.
Data Source
AI summary
A system for measuring, monitoring and controlling the performance of bridges and other infrastructure creates a database for analysis of real time performance and learning through adaptive algorithms allowing the performance to be analyzed over time and for changes in performance against the specific bridge or infrastructure and other bridges or infrastructure in the a network of such infrastructure.