A survey on signal processing methods in fiber optic sensor for oxidized carbon steel
This paper provides a broad overview of the adaptive methods for noise reduction used in the analysis of data in the different sensors such as acoustic emissions sensors, power quality signal analysis. The two algorithms are the Empirical Mode Decomposition and the Ensemble Empirical Mode Decomposit...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Springer Verlag
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048033354&doi=10.1007%2f978-3-319-91192-2_2&partnerID=40&md5=e069929e9c9bcd0859e9ed9b7b4b08f7 http://eprints.utp.edu.my/23586/ |
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| Summary: | This paper provides a broad overview of the adaptive methods for noise reduction used in the analysis of data in the different sensors such as acoustic emissions sensors, power quality signal analysis. The two algorithms are the Empirical Mode Decomposition and the Ensemble Empirical Mode Decomposition. We selected these two algorithms because our focus is on these methods. Firstly, this paper exhibits the inner workings of each algorithm both in the original authors� intuition and the mathematical model utilized. Next, we discuss the advantages of each of the algorithms based on recent and credible research papers and articles. We also critically dissect the limitations of each algorithm. This paper aims to give a general understanding on these algorithms which we hope will spur more research in improving the field of signal processing in the fiber optic sensor for the oxidised carbon steel. © 2019, Springer International Publishing AG, part of Springer Nature. |
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