AI Article Synopsis
- The paper introduces a general model for resistive displacement sensors that adapts to current conditions, addressing wear and failure issues while proposing a fault detection method usable during operation.
- It also presents a self-compensation technique derived from model parameter estimation to enhance sensor accuracy as it experiences wear.
- Validation through simulations and experiments on real sensors indicates high accuracy (97.2%) in fault detection and significant reduction in position estimation errors, from ±15% to approximately ±2% for faulty sensors.
Article Abstract
This paper presents a suitably general model for resistive displacement sensors where the model parameters depend on the current sensor conditions, thereby capturing wearout and failure, and proposes a novel fault detection method that can be seamlessly applied during sensor operation, providing self-diagnostic capabilities. On the basis of the estimation of model parameters, an innovative self-compensation method is derived to increase the accuracy of sensors subject to progressive wearout. The proposed model and methods have been validated by both numerical simulations and experimental tests on two real resistive displacement sensors, placed in undamaged and faulty conditions, respectively. The fault detection method has shown an accuracy of 97.2%. The position estimation error is < ±0.2% of the full-scale span for the undamaged sensor, while the self-compensation method successfully reduces the position estimation error from ±15% to approximately ±2% of the full-scale span for the faulty sensor.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11055168 | PMC |
| http://dx.doi.org/10.3390/s24082594 | DOI Listing |
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