Predictive Sensor State Coding for Water Quality Monitoring
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Solution Overview
Problem
Current environmental monitoring systems lack effective methods for accurately assessing the operational state and reliability of water quality sensors, leading to potential inaccuracies in environmental data collection and maintenance scheduling.
Innovation Solution
The implementation of a predictive model using artificial neural networks that analyzes configuration, measurement, and circuitry information to generate codes representing operational states of sensors, enabling predictive maintenance and data quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used without predictive modeling, then system complexity remains low, but data integrity and reliability assessment capability deteriorate
Solution Approach 1:
The system performs preliminary analysis by generating operational state codes from configuration, measurement, and circuitry information before actual data collection issues arise. The predictive model proactively identifies potential sensor problems and operational states, allowing preventive maintenance rather than reactive responses.
Solution Approach 2:
The patent introduces an intermediary predictive modeling layer between the raw sensor data and the final reliability assessment. This intermediary system processes configuration, measurement, and circuitry information through neural networks to generate operational state codes, which then inform data integrity evaluations without requiring direct complex analysis of all raw parameters.
2Measurement precision
If comprehensive monitoring of all sensor parameters is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system extracts only the most critical features from the comprehensive sensor data set to generate operational state codes. Instead of analyzing all raw parameters directly, the predictive model identifies and extracts key indicators from configuration, measurement, and circuitry information that most significantly indicate sensor operational states and potential issues.
Solution Approach 2:
The patent applies local quality by focusing monitoring efforts on specific critical operational states and parameters rather than uniformly monitoring all sensor outputs. The system identifies which sensors and parameters require heightened attention based on their operational codes, allocating analytical resources to the most significant indicators of data quality and sensor health.
Data Source
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AI summary
A method comprises receiving information associated with operational states of a plurality of apparatuses, wherein each of the apparatuses comprises: a controller; memory accessible to the controller; a bus operatively coupled to the controller; and sensor circuitry operatively coupled to the bus, wherein the sensor circuitry generates measurement information representative of an environmental condition. The method further comprises generating a predictive model based at least in part on the information; and deriving codes based at least in part on the predictive model, wherein each of the codes corresponds to an apparatus-detectable individual operational state. The generating a predictive model preferably comprises: training an artificial neural network; and/or using predictive analytics, in particular using machine learning and data mining.