Industrial Sensor Data Collection With Pattern-Based Adaptive Sampling
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting and utilizing data from multiple sensors due to varying computing resources, network connectivity issues, and complex sensing requirements, leading to conservative and inflexible sensing configurations that fail to detect essential parameters in real-time.
Innovation Solution
The implementation of methods and systems for data collection and processing that include continuous ultrasonic monitoring, self-organizing data marketplaces, on-device sensor fusion, and AI training based on industry-specific feedback, along with augmented reality and virtual reality interfaces for improved data analysis and real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected continuously from multiple sensors in industrial environments, then measurement precision and reliability improve, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the sensing system into modular sensor units that can be independently configured and managed. Each sensor targets specific parameters, allowing the system to divide complex monitoring tasks into manageable segments, reducing overall system complexity while maintaining detection accuracy.
Solution Approach 2:
The patent implements universal sensor configurations that can detect multiple parameters simultaneously. A single sensor unit can monitor various industrial parameters (temperature, pressure, vibration, etc.), reducing the number of specialized sensors needed and simplifying the sensing configuration while improving measurement coverage.
2Productivity
If sensing configurations are made flexible and adaptive to detect parameters in real-time, then productivity and response time improve, but device complexity increases
Solution Approach 1:
The patent implements dynamic sensing configurations that can adapt in real-time based on industrial process conditions. The system automatically adjusts which parameters are monitored and at what frequency, enabling flexible response to changing conditions while maintaining productivity through automated adaptation rather than manual reconfiguration.
Solution Approach 2:
The sensing system incorporates self-configuration capabilities where sensors automatically adjust their monitoring parameters based on detected conditions. The system performs self-diagnosis and self-optimization, reducing the need for complex external control while improving productivity through autonomous adaptation.
3Device complexity
If conservative sensing configurations are used to simplify system design, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent applies local quality by configuring sensors with specific detection capabilities matched to particular industrial parameters and locations. Each sensor is optimized for its specific monitoring task, ensuring high detection reliability for critical parameters while keeping individual sensor configurations simple and manageable.
Data Source
AI summary
The present disclosure describes monitoring systems for data collection in an industrial environment. A system can include a data collection circuit to collect output data from a plurality of sensors such as vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment. The sensors may be communicatively coupled to a data collection circuit, and a machine learning data analysis circuit may receive the output data and learn received data patterns predictive of at least one of an outcome and a state. The monitoring system may determine if the output data matches a learned received output data pattern, wherein the data collection circuit collects data points from sensors based on the learned received output data patterns, the outcome, or the state.