Modular Neural Networks for Real-Time Industrial Sensor Data
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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 real-time monitoring and optimization of complex industrial processes.
Innovation Solution
An expert system utilizing modular neural networks for pattern recognition and self-organization in industrial environments, enabling autonomous control and data marketplace optimization, and integrating data from multiple sources for improved monitoring and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data collection methods are used in industrial environments, then data can be collected from sensors, but real-time monitoring and processing efficiency deteriorate due to manual batch processing and limited automation
Solution Approach 1:
The system employs self-organizing neural networks that automatically configure and optimize data collection from sensor swarms without human intervention. The neural networks autonomously determine which sensors to activate, how to route data, and how to process information in real-time, enabling the system to serve itself and eliminating manual batch processing delays
Solution Approach 2:
The patent applies accelerated processing through neural network-based real-time analysis that dramatically speeds up data processing compared to traditional methods. The neural networks continuously analyze sensor data streams instantaneously, enabling real-time monitoring and decision-making that accelerates the entire industrial process from data collection to actionable insights
2Adaptability or versatility
If fixed sensor configurations are used, then device complexity is reduced, but adaptability to varying operating conditions deteriorates
Solution Approach 1:
The system uses dynamic, self-organizing neural networks that continuously adapt their configuration based on operating conditions. The neural networks dynamically reconfigure which sensors are active, how data is routed, and what analysis is performed in real-time, allowing the system to adapt to varying industrial conditions without manual reconfiguration
Solution Approach 2:
The patent implements feedback mechanisms where neural networks continuously monitor operating conditions and use this information to automatically adjust sensor configurations and data collection strategies. This closed-loop feedback enables the system to adapt to changing conditions while the neural networks manage the complexity of coordination
3Loss of information
If comprehensive data collection from all sensors is performed, then measurement completeness is improved, but data processing complexity and energy consumption increase
Solution Approach 1:
The neural networks extract and prioritize only the most relevant data from the sensor swarm based on current operating conditions and objectives. Instead of processing all sensor data uniformly, the system intelligently extracts critical information while filtering out redundant data, reducing processing energy while maintaining data completeness for decision-making
Solution Approach 2:
The patent applies local quality by having different neural networks handle different data streams and sensor types according to their specific characteristics and importance. Each neural network is optimized for particular data qualities and processing requirements, enabling efficient processing of comprehensive data without uniform high energy consumption across all processing paths
4Extent of automation
If manual data analysis is used, then system complexity is reduced, but automation level and real-time decision-making capability deteriorate
Solution Approach 1:
The patent segments the complex automation task into multiple specialized neural networks, each handling specific functions such as data collection, pattern recognition, prediction, and control decisions. This segmentation of computational tasks into modular neural network components enables high-level automation while organizing system complexity into manageable, functionally-specific units
Data Source
AI summary
Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.