Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study

Moussa Hamadache; Dongik Lee

doi:10.1109/TENCONSpring.2016.7519434

Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study

Moussa Hamadache, Dongik Lee

School of Electronics Engineering

Kyungpook National University

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

2 Scopus citations

Abstract

In this paper, a comparative experimental study between the Parameter Estimation (PE) technique and the Absolute Value Principal Component Analysis (AVPCA) algorithm to bearing fault detection using rotor speed signal monitoring is represented. The PE technique relies on the residuals between the input/output (Voltage/Speed) signals of the real system and of the estimated model. AVPCA, in other hand base on the Sum Square Error (SSE) distance between the training-databases and the tested-databases from just only the output signal (Speed) and its minimum. The experimental results reveal that the AVPCA algorithm is more effective in detecting bearing faults than the PE technique using rotor speed signal monitoring.

Original language	English
Title of host publication	Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	367-372
Number of pages	6
ISBN (Electronic)	9781509009312
DOIs	https://doi.org/10.1109/TENCONSpring.2016.7519434
State	Published - 22 Jul 2016
Event	2016 IEEE Region 10 Symposium, TENSYMP 2016 - Bali, Indonesia Duration: 9 May 2016 → 11 May 2016

Publication series

Name	Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016

Conference

Conference	2016 IEEE Region 10 Symposium, TENSYMP 2016
Country/Territory	Indonesia
City	Bali
Period	9/05/16 → 11/05/16

Keywords

absolute value principal component analysis
bearing fault detection
parameter estimation
rotor speed signal monitoring

Access to Document

10.1109/TENCONSpring.2016.7519434

Cite this

Hamadache, M., & Lee, D. (2016). Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study. In Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016 (pp. 367-372). Article 7519434 (Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCONSpring.2016.7519434

Hamadache, Moussa ; Lee, Dongik. / Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study. Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 367-372 (Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016).

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title = "Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study",

abstract = "In this paper, a comparative experimental study between the Parameter Estimation (PE) technique and the Absolute Value Principal Component Analysis (AVPCA) algorithm to bearing fault detection using rotor speed signal monitoring is represented. The PE technique relies on the residuals between the input/output (Voltage/Speed) signals of the real system and of the estimated model. AVPCA, in other hand base on the Sum Square Error (SSE) distance between the training-databases and the tested-databases from just only the output signal (Speed) and its minimum. The experimental results reveal that the AVPCA algorithm is more effective in detecting bearing faults than the PE technique using rotor speed signal monitoring.",

keywords = "absolute value principal component analysis, bearing fault detection, parameter estimation, rotor speed signal monitoring",

author = "Moussa Hamadache and Dongik Lee",

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Hamadache, M & Lee, D 2016, Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study. in Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016., 7519434, Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016, Institute of Electrical and Electronics Engineers Inc., pp. 367-372, 2016 IEEE Region 10 Symposium, TENSYMP 2016, Bali, Indonesia, 9/05/16. https://doi.org/10.1109/TENCONSpring.2016.7519434

Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study. / Hamadache, Moussa; Lee, Dongik.
Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 367-372 7519434 (Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016).

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

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T1 - Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study

AU - Hamadache, Moussa

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N2 - In this paper, a comparative experimental study between the Parameter Estimation (PE) technique and the Absolute Value Principal Component Analysis (AVPCA) algorithm to bearing fault detection using rotor speed signal monitoring is represented. The PE technique relies on the residuals between the input/output (Voltage/Speed) signals of the real system and of the estimated model. AVPCA, in other hand base on the Sum Square Error (SSE) distance between the training-databases and the tested-databases from just only the output signal (Speed) and its minimum. The experimental results reveal that the AVPCA algorithm is more effective in detecting bearing faults than the PE technique using rotor speed signal monitoring.

AB - In this paper, a comparative experimental study between the Parameter Estimation (PE) technique and the Absolute Value Principal Component Analysis (AVPCA) algorithm to bearing fault detection using rotor speed signal monitoring is represented. The PE technique relies on the residuals between the input/output (Voltage/Speed) signals of the real system and of the estimated model. AVPCA, in other hand base on the Sum Square Error (SSE) distance between the training-databases and the tested-databases from just only the output signal (Speed) and its minimum. The experimental results reveal that the AVPCA algorithm is more effective in detecting bearing faults than the PE technique using rotor speed signal monitoring.

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Hamadache M, Lee D. Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study. In Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 367-372. 7519434. (Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016). doi: 10.1109/TENCONSpring.2016.7519434

Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring - A comparative study

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