A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery

Maamer  Ali Saud Al Tobi; Geraint Bevan; Peter Wallace; David Harrison; K. P. Ramachandran

A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery

Maamer Ali Saud Al Tobi^*, Geraint Bevan, Peter Wallace, David Harrison, K. P. Ramachandran

^*Corresponding author for this work

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

896 Downloads (Pure)

Abstract

Rotating machines play a vital role in many process industries. Vibration analysis is a common form of monitoring their condition. This paper reviews the application of wavelet transforms and artificial intelligence, an advanced form of vibration analysis, for condition monitoring of rotating machines. The review considers different feature extraction methods and shows how wavelet transforms have been applied as a preprocessor for feature extraction with different families of mother wavelet function; and how different artificial intelligence methods have been used for fault classification. It concludes with remarks on the advantages and disadvantages of the applied methods and consideration of future developments to address the current gaps.

Original language	English
Pages (from-to)	70-82
Number of pages	13
Journal	International Journal of Industrial Electronics and Electrical Engineering
Volume	4
Issue number	9
Early online date	30 Sept 2016
Publication status	Published - 24 Oct 2016

Keywords

rotating machinery
wavelet transform
artificial intelligence
genetic algorithm
accuracy rate

Documents and Links

Saud Al Tobi, M.A. (2016) A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery
This is the author accepted manuscript of the article: Al Tobi, M. A. S., Bevan, G., Wallace, P., Harrison, D., & Ramachandran, K. P. (2016). A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery. International Journal of Industrial Electronics and Electrical Engineering, 4(9), 70-82. ( Sep, 2016 ) Copyright © 2016 International Journal of Industrial Electronics and Electrical Engineering (IJIEEE), All Rights Reserved.
Author accepted manuscript, 1.23 MB

http://ijieee.org.in/paper_detail.php?paper_id=5761&name=A_Review_on_Applications_of_Wavelet_Transform_and_Artificial_Intelligence_Systems_in_Fault_Diagnosis_of_Rotating_Machinery

2 Article
1 Book
1 Chapter (peer-reviewed)

Artificial Intelligence Methods for Fault Diagnosis in Centrifugal Pumps
Ali Saud Al Tobi, M., Bevan, G. & Okedu, K., Jul 2022, American Institute of Physics. 302 p.
Research output: Book/Report › Book
Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump
Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Okedu, K. E., Jun 2022, In: Energy Science & Engineering. 10, 6, p. 1826-1839 14 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
8 Citations (Scopus)

157 Downloads (Pure)
Centrifugal pump condition monitoring and diagnosis using frequency domain analysis
Ali Saud Al Tobi, M., Bevan, G., Wallace, P., Harrison, D. & Ramachandran, K. P., Jan 2019, Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Sixth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018, 20-22 June, 2018, Santander, Spain. Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R. & Haddar, M. (eds.). 1st ed. Springer Nature, p. 122-131 10 p. (Applied Condition Monitoring; vol. 15).
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
6 Citations (Scopus)

Peter Wallace
- pwagcu.acuk
- Instrumentation and Control - Senior Lecturer
Person

Cite this

Ali Saud Al Tobi, M., Bevan, G., Wallace, P., Harrison, D., & Ramachandran, K. P. (2016). A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery. International Journal of Industrial Electronics and Electrical Engineering, 4(9), 70-82. http://ijieee.org.in/paper_detail.php?paper_id=5761&name=A_Review_on_Applications_of_Wavelet_Transform_and_Artificial_Intelligence_Systems_in_Fault_Diagnosis_of_Rotating_Machinery

@article{062d2c11bec244c2ae836dcb7d69dfb3,

title = "A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery",

abstract = "Rotating machines play a vital role in many process industries. Vibration analysis is a common form of monitoring their condition. This paper reviews the application of wavelet transforms and artificial intelligence, an advanced form of vibration analysis, for condition monitoring of rotating machines. The review considers different feature extraction methods and shows how wavelet transforms have been applied as a preprocessor for feature extraction with different families of mother wavelet function; and how different artificial intelligence methods have been used for fault classification. It concludes with remarks on the advantages and disadvantages of the applied methods and consideration of future developments to address the current gaps. ",

keywords = "rotating machinery, wavelet transform, artificial intelligence, genetic algorithm, accuracy rate",

author = "{Ali Saud Al Tobi}, Maamer and Geraint Bevan and Peter Wallace and David Harrison and Ramachandran, {K. P.}",

note = "Published Sept.16 Unknown journal policy, query to publisher x 2 8-3-17 and 14-8-17 No DOI VoR available from journal webpage Exception rc'd 8-9-17 ^Exception status: author email in SAN; agreed no exception can be applied, all authors at GCU (library exception review, October 2018)",

year = "2016",

month = oct,

day = "24",

language = "English",

volume = "4",

pages = "70--82",

journal = "International Journal of Industrial Electronics and Electrical Engineering",

issn = "2347-6982",

publisher = "IJIEEE",

number = "9",

}

Ali Saud Al Tobi, M, Bevan, G , Wallace, P, Harrison, D & Ramachandran, KP 2016, 'A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery', International Journal of Industrial Electronics and Electrical Engineering, vol. 4, no. 9, pp. 70-82. <http://ijieee.org.in/paper_detail.php?paper_id=5761&name=A_Review_on_Applications_of_Wavelet_Transform_and_Artificial_Intelligence_Systems_in_Fault_Diagnosis_of_Rotating_Machinery>

A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery. / Ali Saud Al Tobi, Maamer; Bevan, Geraint ; Wallace, Peter et al.
In: International Journal of Industrial Electronics and Electrical Engineering, Vol. 4, No. 9, 24.10.2016, p. 70-82.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery

AU - Ali Saud Al Tobi, Maamer

AU - Bevan, Geraint

AU - Wallace, Peter

AU - Harrison, David

AU - Ramachandran, K. P.

N1 - Published Sept.16 Unknown journal policy, query to publisher x 2 8-3-17 and 14-8-17 No DOI VoR available from journal webpage Exception rc'd 8-9-17 ^Exception status: author email in SAN; agreed no exception can be applied, all authors at GCU (library exception review, October 2018)

PY - 2016/10/24

Y1 - 2016/10/24

N2 - Rotating machines play a vital role in many process industries. Vibration analysis is a common form of monitoring their condition. This paper reviews the application of wavelet transforms and artificial intelligence, an advanced form of vibration analysis, for condition monitoring of rotating machines. The review considers different feature extraction methods and shows how wavelet transforms have been applied as a preprocessor for feature extraction with different families of mother wavelet function; and how different artificial intelligence methods have been used for fault classification. It concludes with remarks on the advantages and disadvantages of the applied methods and consideration of future developments to address the current gaps.

AB - Rotating machines play a vital role in many process industries. Vibration analysis is a common form of monitoring their condition. This paper reviews the application of wavelet transforms and artificial intelligence, an advanced form of vibration analysis, for condition monitoring of rotating machines. The review considers different feature extraction methods and shows how wavelet transforms have been applied as a preprocessor for feature extraction with different families of mother wavelet function; and how different artificial intelligence methods have been used for fault classification. It concludes with remarks on the advantages and disadvantages of the applied methods and consideration of future developments to address the current gaps.

KW - rotating machinery

KW - wavelet transform

KW - artificial intelligence

KW - genetic algorithm

KW - accuracy rate

M3 - Article

SN - 2347-6982

VL - 4

SP - 70

EP - 82

JO - International Journal of Industrial Electronics and Electrical Engineering

JF - International Journal of Industrial Electronics and Electrical Engineering

IS - 9

ER -

A review on applications of wavelet transform and artificial intelligence systems in fault diagnosis of rotating machinery

Abstract

Keywords

Documents and Links

Fingerprint

Research output

Artificial Intelligence Methods for Fault Diagnosis in Centrifugal Pumps

Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump

Centrifugal pump condition monitoring and diagnosis using frequency domain analysis

Profiles

Peter Wallace

Cite this