Self-Checking Sensor Network State Characterization
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Solution Overview
Problem
Existing systems that rely on sensor networks for monitoring and control face challenges in verifying sensor output without using redundant sensors, which increases system complexity and cost.
Innovation Solution
A method for characterizing the operation of sensors in one of a plurality of states, allowing for the evaluation of sensor degradation based on the states of other sensors in the system, thereby eliminating the need for redundant sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If redundant sensors are added to verify sensor output, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The sensor network performs self-diagnosis by having each sensor evaluate its own operational state based on comparisons with other sensors. The controller receives sensor outputs and determines whether each sensor is in a normal or degraded state by comparing its output to other sensors, enabling the system to self-verify without external redundant components.
Solution Approach 2:
Sensors in the network serve multiple functions: they monitor their designated parameters while simultaneously serving as reference sensors for evaluating other sensors. Each sensor's output is used both for its primary measurement function and for cross-validation of other sensors in the network, maximizing the utility of existing sensors.
2Reliability
If redundant sensors are added to verify sensor output, then reliability is improved, but manufacturing cost increases
Solution Approach 1:
The system achieves reliability through self-diagnosis capabilities where the existing sensor network evaluates its own operational states. The controller automatically compares sensor outputs and identifies degraded sensors without requiring additional redundant sensors, thereby maintaining reliability while avoiding the manufacturing costs of extra components.
Solution Approach 2:
Instead of physically copying sensors through redundancy, the system creates virtual references by comparing sensor outputs mathematically. The controller uses the outputs of existing sensors to establish reference values and evaluate other sensors against these references, achieving verification through data copying and comparison rather than physical sensor duplication.
3Measurement precision
If sensor self-reset is performed to compensate for sensor drift, then measurement precision is improved, but reliability worsens due to potential false resets
Solution Approach 1:
The controller continuously monitors sensor outputs and provides feedback about the operational state of each sensor. By comparing sensor outputs in real-time and identifying when sensors enter degraded states, the system can trigger appropriate responses including targeted resets only for sensors that actually need them, rather than performing blanket resets that could cause false corrections.
Solution Approach 2:
The system performs preliminary evaluation of sensor operational states by comparing outputs before initiating any reset actions. The controller determines whether sensors are in normal or degraded states through comparison, and only triggers resets for sensors that show actual degradation, preventing premature or false resets that would occur without this preliminary assessment.
Data Source
AI summary
Methods and systems for operating a system that includes an array of sensors are described. In one example, output of sensors included in the array is characterized as being in one of a plurality of states. Diagnostics may be performed on sensors in the array of sensors based on sensors being in one of the plurality of states.