Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets

Ali Moshrefzadeh; Alessandro Fasana; Luigi Garibaldi

doi:10.1007/978-3-030-11220-2_14

Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets

Ali Moshrefzadeh, Alessandro Fasana, Luigi Garibaldi

Polytechnic University of Turin

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Citations (Scopus)

Abstract

Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.

Original language	English
Title of host publication	Advances in Condition Monitoring of Machinery in Non-Stationary Operations
Subtitle of host publication	Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018
Editors	A. Fernandez Del Rincon, F. Viadero Rueda, F. Chaari, R. Zimroz, M. Haddar
Publisher	Springer, Cham
Pages	132-141
Number of pages	10
Volume	15
ISBN (Electronic)	9783030112202
ISBN (Print)	9783030112196
DOIs	https://doi.org/10.1007/978-3-030-11220-2_14
Publication status	Published - 8 Feb 2019
Externally published	Yes
Event	6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations - Santander, Spain Duration: 20 Jun 2018 → 22 Jun 2018 Conference number: 6 https://cmmno2018.unican.es/inicio2

Publication series

Name	Applied Condition Monitoring
Publisher	Springer
Volume	15
ISSN (Print)	2363-698X
ISSN (Electronic)	2363-6998

Conference

Conference	6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations
Abbreviated title	CMMNO'2018
Country/Territory	Spain
City	Santander
Period	20/06/18 → 22/06/18
Internet address	https://cmmno2018.unican.es/inicio2

Access to Document

10.1007/978-3-030-11220-2_14Licence: Unspecified

Cite this

Moshrefzadeh, A., Fasana, A., & Garibaldi, L. (2019). Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets. In A. Fernandez Del Rincon, F. Viadero Rueda, F. Chaari, R. Zimroz, & M. Haddar (Eds.), Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018 (Vol. 15, pp. 132-141). (Applied Condition Monitoring; Vol. 15). Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_14

Moshrefzadeh, Ali ; Fasana, Alessandro ; Garibaldi, Luigi. / Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets. Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018. editor / A. Fernandez Del Rincon ; F. Viadero Rueda ; F. Chaari ; R. Zimroz ; M. Haddar. Vol. 15 Springer, Cham, 2019. pp. 132-141 (Applied Condition Monitoring).

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title = "Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets",

abstract = "Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.",

keywords = "Autogram, Diagnosis, Experimental data, Fast Kurtogram, Fast Spectral Correlation, Rolling element bearing",

author = "Ali Moshrefzadeh and Alessandro Fasana and Luigi Garibaldi",

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booktitle = "Advances in Condition Monitoring of Machinery in Non-Stationary Operations",

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Moshrefzadeh, A, Fasana, A & Garibaldi, L 2019, Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets. in A Fernandez Del Rincon, F Viadero Rueda, F Chaari, R Zimroz & M Haddar (eds), Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018. vol. 15, Applied Condition Monitoring, vol. 15, Springer, Cham, pp. 132-141, 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, Santander, Spain, 20/06/18. https://doi.org/10.1007/978-3-030-11220-2_14

Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets. / Moshrefzadeh, Ali; Fasana, Alessandro; Garibaldi, Luigi.
Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018. ed. / A. Fernandez Del Rincon; F. Viadero Rueda; F. Chaari; R. Zimroz; M. Haddar. Vol. 15 Springer, Cham, 2019. p. 132-141 (Applied Condition Monitoring; Vol. 15).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets

AU - Moshrefzadeh, Ali

AU - Fasana, Alessandro

AU - Garibaldi, Luigi

N1 - Conference code: 6

PY - 2019/2/8

Y1 - 2019/2/8

N2 - Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.

AB - Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.

KW - Autogram

KW - Diagnosis

KW - Experimental data

KW - Fast Kurtogram

KW - Fast Spectral Correlation

KW - Rolling element bearing

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T3 - Applied Condition Monitoring

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Moshrefzadeh A, Fasana A, Garibaldi L. Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets. In Fernandez Del Rincon A, Viadero Rueda F, Chaari F, Zimroz R, Haddar M, editors, Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO’2018. Vol. 15. Springer, Cham. 2019. p. 132-141. (Applied Condition Monitoring). Epub 2019 Feb 8. doi: 10.1007/978-3-030-11220-2_14

Analysis of Autogram Performance for Rolling Element Bearing Diagnosis by Using Different Data Sets

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