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

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 8; p. 2594
• Main Authors: Mazzoli, Federico, Alghisi, Davide, Ferrari, Vittorio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.04.2024; MDPI
• Subjects: Failure; Methods; Numerical analysis; Parameter estimation; Preventive maintenance; resistive displacement sensors; self-validating sensor; Sensors; smart sensor; resistive displacement sensors; self-validating sensor; smart sensor
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24082594

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.