Adaptive Sensor Parameter Control for Predicted Component States
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting, processing, and utilizing data from multiple sensors due to varying network connectivity, noise sources, and equipment upgrades, leading to inflexible sensing configurations and limited integration of data from similar components, which hampers real-time monitoring and optimization.
Innovation Solution
A system with a data collector, storage, and analysis circuit that adjusts operational processes in response to detected conditions, utilizing neural networks and expert systems to analyze sensor data, and a crosspoint switch for dynamic routing of analog signals, enabling real-time data integration and adaptive data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data collection systems use fixed sensing configurations, then system complexity is reduced, but adaptability to varying network connectivity, noise sources, and equipment upgrades deteriorates
Solution Approach 1:
The patent implements dynamic sensing configurations that automatically adjust data collection parameters based on real-time conditions. The system modifies sampling rates, sensor activation, and data transmission frequency in response to network connectivity changes, noise levels, and equipment state, transforming a static configuration into an adaptive one that optimizes performance without requiring complex manual reconfiguration.
Solution Approach 2:
The system changes operational parameters such as sampling frequency, data resolution, and transmission intervals based on detected conditions. When network connectivity is poor, the system reduces data transmission frequency; when noise levels increase, it adjusts sampling rates to capture relevant signals more frequently, allowing the same hardware to adapt to varying environments without increasing physical complexity.
2Speed
If real-time data processing is implemented, then monitoring responsiveness is improved, but computational resource requirements increase
Solution Approach 1:
The system processes only the necessary subset of data in real-time rather than analyzing all collected information at full speed. It prioritizes critical parameters and anomalies for immediate processing while deferring less urgent data analysis, achieving responsive monitoring without requiring computational resources to process every data point at maximum speed.
Solution Approach 2:
The system performs preliminary filtering and preprocessing of sensor data before full analysis. It identifies and flags anomalies, trends, or critical conditions during initial processing passes, allowing the main processing system to focus only on significant events rather than analyzing every data point in real-time, thus reducing computational load while maintaining monitoring responsiveness.
3Measurement precision
If comprehensive sensor data is collected from all components, then diagnostic accuracy is improved, but data integration complexity increases
Solution Approach 1:
The patent divides the data integration process into segmented modules that handle specific sensor types or equipment categories separately. Each module processes and validates its designated data stream independently, then integrates results at a higher level. This segmentation maintains diagnostic accuracy by ensuring thorough processing of all sensor data while reducing overall integration complexity through modular organization.
Solution Approach 2:
The system implements a universal data integration framework that handles multiple sensor types and data formats through a common processing architecture. Rather than creating separate integration paths for each sensor category, a multi-functional integration layer processes diverse data streams using standardized protocols and algorithms, reducing integration complexity while maintaining the ability to incorporate comprehensive sensor data for accurate diagnostics.
Data Source
AI summary
Systems, methods, and apparatus for adjusting parameters in response to a predicted anticipated state of a component are described. A system may have a data collector to collect sensor data from an industrial environment and a controller to process the data. A collection parameter for one of the input sensors may be determined, an output data pattern recognized, and a signature selected from a plurality of signatures associated with output data patterns. A neural network, trained on signatures associated with output patterns to detect a state of the industrial environment, may predict an anticipated state of the environment and a parameter may be adjusted accordingly.