Cyber-Physical System Sensor Optimization via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cyber-physical systems face challenges in continuously optimizing performance due to changing environmental conditions and system deterioration, as existing methods fail to adaptively adjust sensor settings in real-time to maintain optimal output.
Innovation Solution
A machine learning model, specifically an artificial neural network with radial basis function neurons, is trained using sensor data to generate parameters for controllable sensors, allowing for continuous optimization of performance indicators by distinguishing between controllable and non-controllable sensors and applying these parameters to optimize the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional monitoring methods are used to track physical conditions, then the system can gather information about system state, but the system cannot continuously optimize performance in real-time due to changing environmental conditions and system deterioration
Solution Approach 1:
The patent implements dynamic optimization by continuously updating sensor parameters based on real-time sensor data and machine learning model predictions. The system adapts sensor settings dynamically rather than using static configurations, allowing the cyber-physical system to maintain optimal performance despite changing environmental conditions and system deterioration over time.
Solution Approach 2:
The system employs feedback mechanisms where sensor data is continuously collected, processed through a machine learning model, and used to adjust sensor parameters. This closed-loop feedback enables the system to learn from actual performance data and automatically optimize sensor settings to maintain reliable operation under varying conditions.
2Productivity
If sensor parameters are manually configured, then the system can operate with simple setup, but the system fails to adaptively adjust sensor settings to optimize performance indicators in real-time
Solution Approach 1:
The system performs self-optimization by automatically adjusting sensor parameters based on machine learning model predictions. The machine learning model autonomously determines optimal sensor settings without requiring manual intervention, enabling the system to maximize performance indicators while minimizing operational complexity. The system serves itself by continuously learning from sensor data and autonomously configuring optimal parameters.
3Device complexity
If all sensors are treated equally, then the system can simplify the optimization process, but the system cannot distinguish between controllable and non-controllable sensors to effectively optimize performance
Solution Approach 1:
The patent segments sensors into controllable and non-controllable categories, applying different optimization strategies to each group. Controllable sensors have their parameters actively adjusted by the machine learning model to optimize performance indicators, while non-controllable sensors are monitored but not directly optimized. This segmentation enables precise optimization of measurable parameters while maintaining a manageable optimization process.
Data Source
AI summary
Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.