Detection of patterns in pressure signal of compressed air system using wavelet transform

Mohamad Thabet, David Sanders, Nils Bausch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

77 Downloads (Pure)


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 languageEnglish
Title of host publicationEnergy and Sustainable Futures
Subtitle of host publicationProceedings of 2nd ICESF 2020
EditorsIosif Mporas, Pandelis Kourtessis, Amin Al-Habaibeh, Abhishek Asthana, Vladimir Vukovic, John Senior
ISBN (Electronic)9783030639167
ISBN (Print)9783030639150, 9783030639181
Publication statusPublished - 30 Apr 2021
Event2nd International Conference on Energy and Sustainable Futures: ICESF - University of Hertfordshire
Duration: 10 Sept 202011 Sept 2020

Publication series

NameSpringer Proceedings in Energy
ISSN (Print)2352-2534
ISSN (Electronic)2352-2542


Conference2nd International Conference on Energy and Sustainable Futures


Dive into the research topics of 'Detection of patterns in pressure signal of compressed air system using wavelet transform'. Together they form a unique fingerprint.

Cite this