Self-Organizing Sensor Swarms for Manufacturing Data Collection
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting and analyzing 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 processes.
Innovation Solution
A system for process monitoring that includes a data collector connected to multiple input channels, a data storage unit, a data acquisition circuit, and a response circuit, utilizing neural networks and expert systems to analyze sensor data and adjust operational processes in real-time, while also employing a self-organizing data marketplace for optimized data collection and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from multiple sensors in industrial environments, then monitoring capability is improved, but device complexity increases
Solution Approach 1:
The system segments data collection, processing, and storage functions across distributed edge devices and cloud infrastructure. Each edge device independently collects sensor data from multiple sensors, processes it locally using machine learning models, and transmits only essential results to the cloud, dividing the complex system into manageable autonomous units that collectively achieve comprehensive monitoring
Solution Approach 2:
The patent introduces edge computing devices as intermediaries between sensors and central systems. These edge devices aggregate data from multiple sensors, perform preliminary processing and filtering, and transmit processed information to cloud-based analysis systems, reducing the direct complexity burden on both sensor networks and central processing infrastructure
2Productivity
If real-time data analysis is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system implements periodic data sampling and analysis at edge devices rather than continuous processing. Edge devices collect sensor data at optimized intervals, perform batch processing of accumulated data, and transmit results periodically to cloud systems, achieving real-time monitoring capabilities while reducing peak energy consumption through rhythmic operation patterns
Solution Approach 2:
Edge devices perform self-service data processing using locally deployed machine learning models, eliminating the need to continuously transmit raw sensor data to cloud systems. The edge devices autonomously analyze data, detect anomalies, and generate insights locally, reducing energy consumption associated with constant data transmission and centralized processing
3Adaptability or versatility
If flexible sensing configurations are used, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system employs dynamic sensing configurations where edge devices can adaptively select which sensors to activate and how frequently to sample based on current operational conditions. Machine learning models automatically adjust data collection parameters, sensor selection, and analysis depth according to detected patterns and requirements, providing flexibility without requiring complex manual configuration
Data Source
AI summary
Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.