Cooperative Compressive Sensing for Multi-Sensor Industrial Data
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Solution Overview
Problem
Existing sensing technologies face challenges in efficiently processing and reconstructing data from multiple sensors in industrial environments, particularly in managing data noise, bandwidth conservation, and adaptive sampling techniques to optimize data capture and analysis.
Innovation Solution
The implementation of an analysis system that employs cooperative, adaptive, and compressive sensing techniques, using random sampling and lossy compression to identify and filter data of interest, allowing for efficient data capture, analysis, and storage, while reducing redundant data capture and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional sensing technologies are used to capture data from multiple sensors, then complete data capture is achieved, but data processing and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential features and characteristics from sensor data using compressive sensing techniques. Instead of capturing complete raw data from multiple sensors, the system identifies and extracts key information elements that preserve the essential content while reducing overall data volume significantly.
Solution Approach 2:
The system changes the parameters of data representation by transforming sensor measurements into a compressed domain. Through coordinate transformations and basis changes, the data is represented in a new parameter space where only the most significant components need to be stored, reducing data quantity while maintaining information integrity.
2Productivity
If all sensor data is processed and stored, then comprehensive analysis is possible, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the critical information needed for analysis from the sensor data stream. By identifying and extracting relevant features before processing, the computational burden is reduced while maintaining the comprehensiveness of the analysis for the most important parameters.
Solution Approach 2:
The patent applies partial action by processing only the essential subset of sensor data that provides sufficient information for effective analysis. Rather than exhaustively processing all data from multiple sensors, the system processes a strategically selected portion that achieves the analytical objectives with reduced computational effort and time.
3Measurement precision
If high sampling rates are used to capture dynamic events, then event detection accuracy is improved, but power consumption and data bandwidth requirements increase
Solution Approach 1:
The system employs periodic sampling at variable rates rather than continuous high-rate sampling. By adapting the sampling period based on event detection needs and signal characteristics, the system maintains event detection accuracy during critical moments while reducing power consumption during stable conditions through lower sampling rates.
Solution Approach 2:
The sampling rate is made dynamic rather than static, allowing the system to adjust sampling frequency in real-time based on detected signal changes and event importance. This dynamic adaptation enables high sampling rates only when necessary for accurate event detection, while using lower rates during normal operation to conserve power and reduce bandwidth usage.
Data Source
AI summary
A system for sensing in an industrial environment includes a processing node configured to receive a first sensor data from a first sensor and a second sensor data from a second sensor, both of which are disposed in the industrial environment. The processing node is further configured to process randomly sampled data from at least one of the first sensor data and the second sensor data to generate processed sensor data that includes the randomly sampled data. The processing node further configured to identify data of interest from the processed sensor data based on comparison of the processed sensor data with a sampling dictionary of predetermined information. The processing node further configured to filter and assemble the identified data of interest to create a set of compressed data, and transmit the set of compressed data to a predetermined destination.