Abstract
This paper investigates detecting patterns in the pressure signal of a compressed air system (CAS) with a load/unload control using a wavelet transform. The pressure signal of a CAS carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Three different CAS operating modes were considered: idle, tool activation and faulty. The wavelet transforms of the CAS pressure signal reveal unique features to identify events within each mode. Future work will investigate creating machine learning tools for that utilize these features for fault detection in CAS.
Original language | English |
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Title of host publication | Energy and Sustainable Futures |
Subtitle of host publication | Proceedings of 2nd ICESF 2020 |
Editors | Iosif Mporas, Pandelis Kourtessis, Amin Al-Habaibeh, Abhishek Asthana, Vladimir Vukovic, John Senior |
Publisher | Springer |
Pages | 61-67 |
ISBN (Electronic) | 9783030639167 |
ISBN (Print) | 9783030639150, 9783030639181 |
DOIs | |
Publication status | Published - 30 Apr 2021 |
Event | 2nd International Conference on Energy and Sustainable Futures: ICESF - University of Hertfordshire Duration: 10 Sept 2020 → 11 Sept 2020 |
Publication series
Name | Springer Proceedings in Energy |
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Publisher | Springer |
Volume | 34 |
ISSN (Print) | 2352-2534 |
ISSN (Electronic) | 2352-2542 |
Conference
Conference | 2nd International Conference on Energy and Sustainable Futures |
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Period | 10/09/20 → 11/09/20 |
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Sanders, David (Recipient), 2 Nov 0001
Prize: National/international honour