Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors
An inductive debris sensor can monitor a mechanical system’s debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles’ aliasing behavior. Then,...
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doaj-5bc1bac277c74d72854d828f1e6564312020-11-25T03:33:56ZengMDPI AGSensors1424-82202020-10-01205949594910.3390/s20205949Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris SensorsXingjian Wang0Hanyu Sun1Shaoping Wang2Wenhao Huang3School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaAn inductive debris sensor can monitor a mechanical system’s debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles’ aliasing behavior. Then, a cross-correlation-based method was proposed to deal with this aliasing. Afterwards, taking advantage of the processed signal along with the original signal, an optimization strategy was proposed to make the evaluation of the aliasing debris more accurate than that merely using initial signals. Compared to other methods, the proposed method has fewer limitations in practical applications. The simulation and experimental results also verified the advantage of the proposed method.https://www.mdpi.com/1424-8220/20/20/5949inductive debris sensorsignal aliasingcross-correlation algorithmoptimization strategy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xingjian Wang Hanyu Sun Shaoping Wang Wenhao Huang |
spellingShingle |
Xingjian Wang Hanyu Sun Shaoping Wang Wenhao Huang Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors Sensors inductive debris sensor signal aliasing cross-correlation algorithm optimization strategy |
author_facet |
Xingjian Wang Hanyu Sun Shaoping Wang Wenhao Huang |
author_sort |
Xingjian Wang |
title |
Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors |
title_short |
Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors |
title_full |
Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors |
title_fullStr |
Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors |
title_full_unstemmed |
Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors |
title_sort |
cross-correlation algorithm-based optimization of aliasing signals for inductive debris sensors |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-10-01 |
description |
An inductive debris sensor can monitor a mechanical system’s debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles’ aliasing behavior. Then, a cross-correlation-based method was proposed to deal with this aliasing. Afterwards, taking advantage of the processed signal along with the original signal, an optimization strategy was proposed to make the evaluation of the aliasing debris more accurate than that merely using initial signals. Compared to other methods, the proposed method has fewer limitations in practical applications. The simulation and experimental results also verified the advantage of the proposed method. |
topic |
inductive debris sensor signal aliasing cross-correlation algorithm optimization strategy |
url |
https://www.mdpi.com/1424-8220/20/20/5949 |
work_keys_str_mv |
AT xingjianwang crosscorrelationalgorithmbasedoptimizationofaliasingsignalsforinductivedebrissensors AT hanyusun crosscorrelationalgorithmbasedoptimizationofaliasingsignalsforinductivedebrissensors AT shaopingwang crosscorrelationalgorithmbasedoptimizationofaliasingsignalsforinductivedebrissensors AT wenhaohuang crosscorrelationalgorithmbasedoptimizationofaliasingsignalsforinductivedebrissensors |
_version_ |
1724560739905044480 |