Sensor Drift Diagnosis Using Global Inspection Ranking
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Solution Overview
Problem
Current sensor failure diagnosis methods are inefficient as they require physical inspection and struggle to accurately determine the priority of sensor inspection due to gradual drift failure, leading to increased labor costs and maintenance burdens.
Innovation Solution
A sensor failure diagnosis device that remotely estimates drift failure in sensors and prioritizes inspection based on estimated drift values, using a system configuration with a database, drift estimator, correction module, and ranking determiners to provide global ranks for efficient inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If remote monitoring is implemented to reduce inspection costs, then labor costs are reduced, but measurement precision deteriorates due to inability to physically verify sensor accuracy
Solution Approach 1:
The patent introduces an intermediary measurement device that physically measures the actual value of the measurement target, serving as a mediator between the monitored sensor and the truth. This intermediary device provides reference measurement data that enables remote verification of sensor accuracy without requiring physical inspection workers, thus reducing labor costs while maintaining measurement precision through comparative analysis between the sensor output and intermediary measurements
Solution Approach 2:
The patent replaces the mechanical system of physical inspection (workers traveling to sensor locations) with an automated electronic measurement and comparison system. The measurement device automatically collects data, the determination unit automatically compares sensor values with reference values, and the system automatically generates inspection priorities, eliminating the need for manual physical verification while maintaining accuracy assessment capabilities
2Productivity
If collective inspection of sensor groups is performed to improve work efficiency, then productivity increases, but the ability to identify individual failing sensors deteriorates
Solution Approach 1:
The patent segments the collective sensor group inspection into individual sensor assessments by calculating determination values for each sensor independently. The determination unit processes each sensor's output separately, comparing it with reference values from the measurement device, and generates individual inspection priority information. This segmentation enables identification of specific failing sensors within groups while maintaining the efficiency benefits of remote batch processing
Solution Approach 2:
The patent creates a virtual copy of the physical measurement process by using measurement data from intermediary devices to represent actual conditions. Instead of physically inspecting each sensor, the system creates determination values that replicate the inspection process remotely, allowing individual sensor assessment within collective processing frameworks and enabling precise identification of failing sensors without physical presence
3Ease of operation
If inspection is performed on device basis rather than sensor basis to reduce actions, then ease of operation improves, but reliability of sensor-specific diagnosis deteriorates
Solution Approach 1:
The patent introduces dynamic prioritization that adapts inspection processes based on calculated determination values. Instead of static device-level inspection schedules, the system dynamically adjusts inspection priorities for individual sensors based on their actual performance data and drift characteristics. This dynamic approach maintains operational simplicity through automated ranking while improving diagnostic reliability by focusing resources on sensors with highest failure probability
Solution Approach 2:
The patent changes the parameter of inspection granularity from device-level to sensor-level by introducing determination values that quantify individual sensor status. The system transforms the inspection parameter from coarse device grouping to fine sensor-specific metrics, enabling reliable sensor-specific diagnosis while maintaining ease of operation through automated calculation and ranking of inspection priorities based on drift amounts and determination values
Data Source
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AI summary
According to one embodiment, a sensor failure diagnosis device includes a drift estimator (4) and a global rank determiner (9). The drift estimator estimates, based on data on measured values from sensors contained in a plurality of sensor groups including a sensor under inspection, whether or not drift failure that is steady deviation of a measured value from a true value has occurred in the sensor under inspection or a degree of the drift failure. The global rank determiner determines global ranks that are priorities of the plurality of sensor groups in the inspection based on a plurality of estimated results by the drift estimator.