Adaptive Sensor Parameter Groups for Real-Time Pattern Recognition
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Solution Overview
Problem
Industrial environments face challenges in efficiently collecting and utilizing data from multiple sensors due to limited data range, complexity, and variability in network connectivity, noise, and equipment conditions, leading to conservative sensing configurations that fail to detect essential parameters in real-time.
Innovation Solution
The implementation of methods and systems for continuous ultrasonic monitoring, on-device sensor fusion, self-organizing data marketplaces, and AI training based on industry-specific feedback, along with augmented reality and virtual reality interfaces, to enhance data collection, processing, and intelligent diagnosis in industrial IoT environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conservative sensing configurations are used to simplify data collection, then device complexity is reduced, but measurement precision deteriorates as essential parameters are not detected in real-time
Solution Approach 1:
The system dynamically adjusts sensing configurations based on real-time pattern recognition results. When anomalies are detected, the system automatically activates additional sensors and increases sampling rates, transitioning from a conservative static configuration to an adaptive dynamic configuration that maintains low complexity during normal operation while achieving high measurement precision when needed
Solution Approach 2:
The system implements feedback loops where pattern recognition algorithms continuously analyze sensor data and provide feedback to adjust sensing configurations. This feedback mechanism enables the system to automatically detect when essential parameters need to be monitored and adjust the sensing configuration accordingly, resolving the contradiction between simplicity and detection accuracy
2Loss of information
If data is collected in batches for later analysis, then loss of information is reduced, but loss of time increases as analysis takes weeks or months
Solution Approach 1:
The system segments the data analysis process into multiple levels: edge devices perform real-time pattern recognition on local data, cloud-based systems handle more complex analyses, and historical data is processed in batches. This segmentation enables immediate detection of critical patterns while maintaining comprehensive data collection, reducing both information loss and time delay simultaneously
Solution Approach 2:
The system performs preliminary pattern recognition and filtering at the edge devices before data is transmitted to central systems. This preliminary action prepares data in advance, enabling faster subsequent analysis and reducing the overall time from data collection to actionable insights while maintaining data completeness
3Measurement precision
If the range of sensed parameters is expanded to capture more data, then measurement precision improves, but device complexity increases due to multiple sensors and data variability
Solution Approach 1:
The system employs universal sensor platforms and standardized data processing architectures that can handle multiple parameter types through a single unified system. This multi-functionality approach allows the system to expand its sensing capabilities without proportionally increasing complexity, as the same hardware and software infrastructure processes diverse sensor data streams
Solution Approach 2:
The system changes parameters such as sampling rates, data transmission frequencies, and processing depths based on the specific monitoring needs and anomaly detection results. This dynamic parameter adjustment allows the system to maintain high measurement precision for critical parameters while reducing the processing burden and effective complexity for less critical data streams
Data Source
AI summary
The present disclosure describes systems for data collection in an industrial environment. A system can include an industrial system including a plurality of components, at least one component operatively coupled to a sensor, and a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group. A pattern recognition circuit may determine a recognized pattern value in response to at least a portion of the data values, wherein the recognized pattern value includes a secondary value. A sensor learning circuit may update the sensed parameter group in response to the recognized pattern value and adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group. The pattern recognition circuit and the sensor learning circuit iteratively determine the recognized pattern value and update the sensed parameter group to improve a sensing performance value.