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

Mohamad Thabet; David Sanders; Nils Bausch

doi:10.1007/978-3-030-63916-7_8

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 proceeding › Conference contribution

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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
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	https://doi.org/10.1007/978-3-030-63916-7_8
Publication status	Published - 30 Apr 2021
Event	2nd International Conference on Energy and Sustainable Futures: ICESF - University of Hertfordshire Duration: 10 Sep 2020 → 11 Sep 2020

Publication series

Name	Springer Proceedings in Energy
Publisher	Springer
Volume	34
ISSN (Print)	2352-2534
ISSN (Electronic)	2352-2542

Conference

Conference	2nd International Conference on Energy and Sustainable Futures
Period	10/09/20 → 11/09/20

Access to Document

10.1007/978-3-030-63916-7_8Licence: Unspecified

Detection of Patterns in Pressure SignalFinal published version, 590 KBLicence: CC BY

Cite this

Thabet, M., Sanders, D., & Bausch, N. (2021). Detection of patterns in pressure signal of compressed air system using wavelet transform. In I. Mporas, P. Kourtessis, A. Al-Habaibeh, A. Asthana, V. Vukovic, & J. Senior (Eds.), Energy and Sustainable Futures : Proceedings of 2nd ICESF 2020 (pp. 61-67). (Springer Proceedings in Energy; Vol. 34). Springer. https://doi.org/10.1007/978-3-030-63916-7_8

Thabet, Mohamad ; Sanders, David ; Bausch, Nils. / Detection of patterns in pressure signal of compressed air system using wavelet transform. Energy and Sustainable Futures : Proceedings of 2nd ICESF 2020. editor / Iosif Mporas ; Pandelis Kourtessis ; Amin Al-Habaibeh ; Abhishek Asthana ; Vladimir Vukovic ; John Senior. Springer, 2021. pp. 61-67 (Springer Proceedings in Energy).

@inproceedings{fd43063c500b4ed59298141accb4d0f9,

title = "Detection of patterns in pressure signal of compressed air system using wavelet transform",

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 {\textquoteleft}signatures{\textquoteright} 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.",

author = "Mohamad Thabet and David Sanders and Nils Bausch",

year = "2021",

month = apr,

day = "30",

doi = "10.1007/978-3-030-63916-7_8",

language = "English",

isbn = "9783030639150",

series = "Springer Proceedings in Energy",

publisher = "Springer",

pages = "61--67",

editor = "Iosif Mporas and Pandelis Kourtessis and Amin Al-Habaibeh and Abhishek Asthana and Vladimir Vukovic and John Senior",

booktitle = "Energy and Sustainable Futures",

note = "2nd International Conference on Energy and Sustainable Futures : ICESF ; Conference date: 10-09-2020 Through 11-09-2020",

}

Thabet, M, Sanders, D & Bausch, N 2021, Detection of patterns in pressure signal of compressed air system using wavelet transform. in I Mporas, P Kourtessis, A Al-Habaibeh, A Asthana, V Vukovic & J Senior (eds), Energy and Sustainable Futures : Proceedings of 2nd ICESF 2020. Springer Proceedings in Energy, vol. 34, Springer, pp. 61-67, 2nd International Conference on Energy and Sustainable Futures, 10/09/20. https://doi.org/10.1007/978-3-030-63916-7_8

Detection of patterns in pressure signal of compressed air system using wavelet transform. / Thabet, Mohamad; Sanders, David ; Bausch, Nils.

Energy and Sustainable Futures : Proceedings of 2nd ICESF 2020. ed. / Iosif Mporas; Pandelis Kourtessis; Amin Al-Habaibeh; Abhishek Asthana; Vladimir Vukovic; John Senior. Springer, 2021. p. 61-67 (Springer Proceedings in Energy; Vol. 34).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Detection of patterns in pressure signal of compressed air system using wavelet transform

AU - Thabet, Mohamad

AU - Sanders, David

AU - Bausch, Nils

PY - 2021/4/30

Y1 - 2021/4/30

N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-030-63916-7_8

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SN - 9783030639150

SN - 9783030639181

T3 - Springer Proceedings in Energy

SP - 61

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BT - Energy and Sustainable Futures

A2 - Mporas, Iosif

A2 - Kourtessis, Pandelis

A2 - Al-Habaibeh, Amin

A2 - Asthana, Abhishek

A2 - Vukovic, Vladimir

A2 - Senior, John

PB - Springer

T2 - 2nd International Conference on Energy and Sustainable Futures

Y2 - 10 September 2020 through 11 September 2020

ER -

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

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