Self-Diagnostic Method for Resistive Displacement Sensors
Displacement sensors play a key role in the control of dynamic processes. Such sensors can be endowed with self-diagnostic capabilities to identify both the degradation of their conditions and the possible process anomalies that caused them, thus allowing researchers to monitor the process efficienc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/97/1/164 |
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| author | Federico Mazzoli Davide Alghisi Vittorio Ferrari |
| author_facet | Federico Mazzoli Davide Alghisi Vittorio Ferrari |
| author_sort | Federico Mazzoli |
| collection | DOAJ |
| description | Displacement sensors play a key role in the control of dynamic processes. Such sensors can be endowed with self-diagnostic capabilities to identify both the degradation of their conditions and the possible process anomalies that caused them, thus allowing researchers to monitor the process efficiency and therefore its sustainability. Within this scope, a self-diagnostic method is proposed to infer the conditions of a resistive displacement sensor by estimating its model parameters online during operation. Experimental results confirm the effectiveness of the presented method. |
| format | Article |
| id | doaj-art-00d50d1a804e4428906f3ef2a27317c8 |
| institution | Kabale University |
| issn | 2504-3900 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj-art-00d50d1a804e4428906f3ef2a27317c82024-12-27T14:49:00ZengMDPI AGProceedings2504-39002024-04-0197116410.3390/proceedings2024097164Self-Diagnostic Method for Resistive Displacement SensorsFederico Mazzoli0Davide Alghisi1Vittorio Ferrari2Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyResearch and Development, Gefran SpA, Via Cave 11, 25050 Provaglio d’Iseo, ItalyDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyDisplacement sensors play a key role in the control of dynamic processes. Such sensors can be endowed with self-diagnostic capabilities to identify both the degradation of their conditions and the possible process anomalies that caused them, thus allowing researchers to monitor the process efficiency and therefore its sustainability. Within this scope, a self-diagnostic method is proposed to infer the conditions of a resistive displacement sensor by estimating its model parameters online during operation. Experimental results confirm the effectiveness of the presented method.https://www.mdpi.com/2504-3900/97/1/164smart sensorssustainable sensorsconditions monitoringpredictive maintenance |
| spellingShingle | Federico Mazzoli Davide Alghisi Vittorio Ferrari Self-Diagnostic Method for Resistive Displacement Sensors Proceedings smart sensors sustainable sensors conditions monitoring predictive maintenance |
| title | Self-Diagnostic Method for Resistive Displacement Sensors |
| title_full | Self-Diagnostic Method for Resistive Displacement Sensors |
| title_fullStr | Self-Diagnostic Method for Resistive Displacement Sensors |
| title_full_unstemmed | Self-Diagnostic Method for Resistive Displacement Sensors |
| title_short | Self-Diagnostic Method for Resistive Displacement Sensors |
| title_sort | self diagnostic method for resistive displacement sensors |
| topic | smart sensors sustainable sensors conditions monitoring predictive maintenance |
| url | https://www.mdpi.com/2504-3900/97/1/164 |
| work_keys_str_mv | AT federicomazzoli selfdiagnosticmethodforresistivedisplacementsensors AT davidealghisi selfdiagnosticmethodforresistivedisplacementsensors AT vittorioferrari selfdiagnosticmethodforresistivedisplacementsensors |