Self-Diagnostic and Self-Compensation Methods for Resistive Displacement Sensors Tailored for In-Field Implementation
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 sel...
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| Published in | Sensors (Basel, Switzerland) Vol. 24; no. 8; p. 2594 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
18.04.2024
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s24082594 |
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| 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Alghisi, Davide Ferrari, Vittorio Mazzoli, Federico |
| AuthorAffiliation | 2 Gefran SpA, Via Cave 11, 25050 Provaglio d’Iseo, Italy; davide.alghisi@gefran.com 1 Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy; vittorio.ferrari@unibs.it |
| AuthorAffiliation_xml | – name: 1 Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy; vittorio.ferrari@unibs.it – name: 2 Gefran SpA, Via Cave 11, 25050 Provaglio d’Iseo, Italy; davide.alghisi@gefran.com |
| Author_xml | – sequence: 1 givenname: Federico orcidid: 0000-0002-9892-947X surname: Mazzoli fullname: Mazzoli, Federico – sequence: 2 givenname: Davide surname: Alghisi fullname: Alghisi, Davide – sequence: 3 givenname: Vittorio orcidid: 0000-0002-3949-9975 surname: Ferrari fullname: Ferrari, Vittorio |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38676212$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/electronics9071063 10.1016/0967-0661(93)91382-7 10.1016/j.ins.2020.08.068 10.1016/j.sna.2005.09.056 10.1109/TMECH.2008.2001184 10.1109/CSE.2013.116 10.1016/j.fusengdes.2019.111401 10.1109/TAES.1975.308108 10.1063/1.361943 10.1016/j.conengprac.2011.03.002 10.1016/j.optmat.2007.12.022 10.3390/su12198211 10.1109/19.836318 10.1109/JSEN.2021.3090990 10.3390/proceedings2024097164 10.1109/ISCO.2016.7726957 10.1016/j.arcontrol.2004.12.002 10.1016/0005-1098(86)90031-2 10.1108/JQME-04-2016-0014 10.1016/j.proche.2009.07.125 |
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| Copyright | COPYRIGHT 2024 MDPI AG 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2024 by the authors. 2024 |
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| Keywords | resistive displacement sensors self-validating sensor smart sensor |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The paper is an extended and updated version of the contribution presented by the same authors at Eurosensors XXXV, Lecce, Italy, 10–13 September 2023, entitled “Self-Diagnostic Method for Resistive Displacement Sensors”. |
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| SubjectTerms | Failure Methods Numerical analysis Parameter estimation Preventive maintenance resistive displacement sensors self-validating sensor Sensors smart sensor |
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| Title | Self-Diagnostic and Self-Compensation Methods for Resistive Displacement Sensors Tailored for In-Field Implementation |
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