Degradation Detection in a Redundant Sensor Architecture
Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In ta...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/12/4649 |
| _version_ | 1797482360030101504 |
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| author | Amer Kajmakovic Konrad Diwold Kay Römer Jesus Pestana Nermin Kajtazovic |
| author_facet | Amer Kajmakovic Konrad Diwold Kay Römer Jesus Pestana Nermin Kajtazovic |
| author_sort | Amer Kajmakovic |
| collection | DOAJ |
| description | Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down. |
| first_indexed | 2024-03-09T22:31:11Z |
| format | Article |
| id | doaj.art-b53a8a54ce114264b60731ac1f2889a5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:31:11Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b53a8a54ce114264b60731ac1f2889a52023-11-23T18:56:51ZengMDPI AGSensors1424-82202022-06-012212464910.3390/s22124649Degradation Detection in a Redundant Sensor ArchitectureAmer Kajmakovic0Konrad Diwold1Kay Römer2Jesus Pestana3Nermin Kajtazovic4Pro2Future GmbH, 8010 Graz, AustriaPro2Future GmbH, 8010 Graz, AustriaInstitute of Technical Informatics, Graz University of Technology, 8010 Graz, AustriaPro2Future GmbH, 8010 Graz, AustriaSiemens AG, 8054 Graz, AustriaSafety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down.https://www.mdpi.com/1424-8220/22/12/4649degradationdriftdiscrepancyredundant sensors1oo2 architecture |
| spellingShingle | Amer Kajmakovic Konrad Diwold Kay Römer Jesus Pestana Nermin Kajtazovic Degradation Detection in a Redundant Sensor Architecture Sensors degradation drift discrepancy redundant sensors 1oo2 architecture |
| title | Degradation Detection in a Redundant Sensor Architecture |
| title_full | Degradation Detection in a Redundant Sensor Architecture |
| title_fullStr | Degradation Detection in a Redundant Sensor Architecture |
| title_full_unstemmed | Degradation Detection in a Redundant Sensor Architecture |
| title_short | Degradation Detection in a Redundant Sensor Architecture |
| title_sort | degradation detection in a redundant sensor architecture |
| topic | degradation drift discrepancy redundant sensors 1oo2 architecture |
| url | https://www.mdpi.com/1424-8220/22/12/4649 |
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