IIoT Data Collection Routing Under Network Throughput Limits
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting and processing data from multiple sensors due to varying operating conditions, network connectivity issues, and the need for flexible sensing configurations, which limits effective monitoring and optimization of complex industrial processes.
Innovation Solution
A monitoring system that includes a data collector communicatively coupled to multiple input channels and a network infrastructure, capable of adjusting data collection routines based on throughput parameters, deactivating channels, and redistributing data collection within a swarm of collectors, using neural networks for data analysis and auto-scaling to optimize data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection rate is increased to improve monitoring accuracy, then measurement precision is improved, but network throughput capacity is exceeded causing data loss
Solution Approach 1:
The system dynamically changes data collection parameters (sampling rate, data resolution, collection frequency) based on network conditions and process priorities. When network throughput is limited, the system adjusts parameters to reduce data volume while maintaining critical monitoring accuracy for high-priority processes.
Solution Approach 2:
Different data collection qualities are applied to different input channels based on their priority and importance. Critical processes receive high-precision data collection with higher sampling rates, while less critical processes use reduced data collection rates, optimizing overall network utilization while maintaining essential monitoring accuracy.
2Loss of information
If data collection is increased to capture all sensor data, then information completeness is improved, but network bandwidth consumption increases causing connectivity issues
Solution Approach 1:
The system adjusts data collection parameters dynamically based on network throughput availability. When network bandwidth is constrained, the system reduces sampling rates and data transmission frequency while maintaining information completeness for critical parameters, balancing information needs with network capacity.
Solution Approach 2:
The system collects complete data for high-priority critical processes while using partial data collection for lower-priority processes. This selective approach ensures essential information is captured without overwhelming network bandwidth, applying full data collection only where absolutely necessary.
3Device complexity
If fixed sensing configurations are used to simplify system design, then device complexity is reduced, but adaptability to varying operating conditions deteriorates
Solution Approach 1:
The sensing configuration transitions from static to dynamic, allowing the system to adapt data collection parameters, channel activation, and sampling rates in real-time based on operating conditions, process priorities, and network availability, while maintaining a relatively simple underlying hardware architecture.
Solution Approach 2:
The data collector is designed with universal capabilities to handle multiple input channels and various data collection routines, allowing a single device to adapt to different operating conditions and process requirements without requiring specialized configurations for each scenario.
4Loss of information
If conservative sensing configurations detect many parameters to ensure coverage, then information completeness is improved, but device complexity and data processing burden increase
Solution Approach 1:
Instead of uniformly high data collection across all parameters, the system applies different data collection qualities to different parameters based on their importance. Critical parameters are monitored with high fidelity and continuous sampling, while less critical parameters use reduced sampling rates or event-triggered collection, maintaining comprehensive parameter coverage with reduced overall complexity.
Data Source
AI summary
Monitoring, systems and methods for data collection in an industrial environment are disclosed. A system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data, and sense a change in operation and determine an aggregate rate of data being collected from the plurality of input channels. If the aggregate rate exceeds a throughput parameter the data analysis circuit alters the data collection to reduce the amount of data collected or, based on the sensed change, modify a collector route.