Local Sensor Analytics Feedback Tuning for Detection Accuracy
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Solution Overview
Problem
Existing analytics models embedded in IoT devices are slow to update and lack scalability, leading to imprecision and inefficiency in recognizing errors, particularly in critical applications like maintenance diagnostics and safety monitoring, due to their reliance on pre-computed models and aggregate data, which fail to adapt to changing conditions.
Innovation Solution
A method and system for continuously updating and improving local analytics by selecting models and parameters based on the likelihood of hits, misses, false alarms, and correct rejections, using a processor and database to evaluate sensor data and adjust criteria dynamically, incorporating feedback loops for self-correction and adaptation across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-computed models and aggregate data are used for local analytics, then device complexity is reduced and ease of operation is improved, but measurement precision and reliability deteriorate due to imprecision from assumptions and lack of adaptability
Solution Approach 1:
The system dynamically updates model parameters and selects models based on current operational conditions rather than using static pre-computed models. Local analytics devices continuously adapt their analytical models to changing conditions, transforming the system from static to dynamic to improve measurement precision while maintaining ease of operation through automated adaptation.
Solution Approach 2:
The system implements feedback loops where outcomes from local analytics are continuously monitored and used to update model parameters and select improved models. This feedback mechanism allows the system to learn from actual performance data, correcting imprecision from assumptions by continuously refining models based on real-world outcomes.
2Device complexity
If pre-computed models based on first principles or big data aggregation are used, then device complexity is reduced, but reliability deteriorates due to errors that cannot be recognized or self-corrected
Solution Approach 1:
Local analytics devices perform self-updating of model parameters and self-selection of models based on their own operational outcomes. The system is self-correcting, automatically identifying and fixing errors without external intervention, thereby improving reliability while maintaining low device complexity through autonomous operation.
Solution Approach 2:
The system uses feedback from actual analytics outcomes to continuously validate and correct model performance. By monitoring hits, misses, false alarms, and correct rejections, the system automatically adjusts parameters and selects models that improve reliability, eliminating the need for complex external validation systems.
3Manufacturing precision
If models are updated only in new product generations or tailored for large installations, then manufacturing precision is maintained through controlled updates, but productivity deteriorates due to insufficient scalability for updating and adapting analytical modules
Solution Approach 1:
The system creates a universal framework for model updating that can be applied across all local analytics devices regardless of scale or application. The same automated model selection and parameter update mechanisms work for both individual devices and large installations, eliminating the need for separate update processes and improving productivity while maintaining manufacturing precision through consistent methodologies.
Solution Approach 2:
The system pre-establishes a database of models and outcomes and implements automated selection algorithms that can immediately begin updating analytical modules upon deployment. This preliminary preparation eliminates the need for time-consuming manual tailoring for each installation, dramatically improving productivity while maintaining precision through the pre-vetted model database.
4Measurement precision
If signal detection theory optimization is applied without experimental control, then measurement precision of sensor sensitivity can be improved, but reliability of response criteria optimization deteriorates due to inability to isolate and optimize response criteria independently
Solution Approach 1:
The system segments the optimization process into two independent components: sensor sensitivity optimization and response criteria optimization. By separating these previously confounded elements, the system can independently optimize each aspect using outcome data, improving the reliability of response criteria while maintaining the measurement precision of sensor sensitivity through separate adjustment mechanisms.
Data Source
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AI summary
Systems and methods for dynamically optimizing models used for sensor data analytics. An action is taken based on an analytics determination by systematically varying parameters of the analytical model using actions taken based on the analytics to determine the relative frequencies of hits, misses, false alarms, and correct rejections for particular model parameters. The model parameters for local analytics are selected based upon on signal detection theory analysis and the value or cost of each hit, miss, false alarm, or correct rejection.