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Distance Corrections01:15

Distance Corrections

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Updated: Jun 27, 2025

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Self-Diagnostic and Self-Compensation Methods for Resistive Displacement Sensors Tailored for In-Field

Federico Mazzoli1, Davide Alghisi2, Vittorio Ferrari1

  • 1Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.

Sensors (Basel, Switzerland)
|April 27, 2024
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Summary
This summary is machine-generated.

This study introduces a new model and fault detection method for resistive displacement sensors, enhancing their self-diagnosis and accuracy even with wear and tear. The methods improve performance in both healthy and faulty sensor conditions.

Keywords:
resistive displacement sensorsself-validating sensorsmart sensor

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Area of Science:

  • Sensor technology
  • Mechanical engineering
  • Electrical engineering

Background:

  • Resistive displacement sensors are crucial for accurate measurements.
  • Sensor performance degrades over time due to wear and failure.
  • Existing methods lack robust self-diagnostic and compensation capabilities.

Purpose of the Study:

  • To develop a general model for resistive displacement sensors that accounts for wearout and failure.
  • To propose a novel fault detection method for real-time self-diagnosis.
  • To derive a self-compensation method for improving accuracy in aging sensors.

Main Methods:

  • A general sensor model with condition-dependent parameters was developed.
  • A fault detection algorithm was integrated for seamless operational application.
  • A self-compensation strategy was derived based on estimated model parameters.

Main Results:

  • The fault detection method achieved 97.2% accuracy.
  • Undamaged sensors maintained < ±0.2% position estimation error.
  • Self-compensation reduced error in faulty sensors from ±15% to ~±2%.

Conclusions:

  • The proposed model and methods effectively capture sensor wearout and failure.
  • The fault detection and self-compensation techniques enhance sensor reliability and accuracy.
  • Validated through simulations and experiments on real-world sensors.