Estimating faults modes in ball bearing machinery using a sparse reconstruction framework

Maria Juhlin; Johan Swärd; Marius Pesavento; Andreas Jakobsson

doi:10.23919/EUSIPCO.2018.8552950

Estimating faults modes in ball bearing machinery using a sparse reconstruction framework

Maria Juhlin, Johan Swärd, Marius Pesavento, Andreas Jakobsson

Technical University of Darmstadt

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

Abstract

In this work, we present a computationally efficient algorithm for estimating fault modes in ball bearing systems. The presented method generalizes and improves upon earlier developed sparse reconstruction techniques, allowing for detecting multiple fault modes. The measured signal is corrupted with additive and multiplicative noise, yielding a signal that is highly erratic. Fortunately, the damaged ball bearings give rise to strong periodical structures which may be exploited when forming the proposed detector. Numerical simulations illustrate the preferred performance of the proposed method.

Original language	English
Title of host publication	2018 26th European Signal Processing Conference, EUSIPCO 2018
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	2330-2334
Number of pages	5
Volume	2018-September
ISBN (Electronic)	9789082797015
DOIs	https://doi.org/10.23919/EUSIPCO.2018.8552950
Publication status	Published - 2018 Nov 29
Event	26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy Duration: 2018 Sept 3 → 2018 Sept 7

Conference

Conference	26th European Signal Processing Conference, EUSIPCO 2018
Country/Territory	Italy
City	Rome
Period	2018/09/03 → 2018/09/07

Subject classification (UKÄ)

Signal Processing

Free keywords

ADMM
Ball bearing systems
Convex optimization
Sparse reconstruction

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10.23919/EUSIPCO.2018.8552950

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Juhlin, M., Swärd, J., Pesavento, M., & Jakobsson, A. (2018). Estimating faults modes in ball bearing machinery using a sparse reconstruction framework. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (Vol. 2018-September, pp. 2330-2334). Article 8552950 IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/EUSIPCO.2018.8552950

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title = "Estimating faults modes in ball bearing machinery using a sparse reconstruction framework",

abstract = "In this work, we present a computationally efficient algorithm for estimating fault modes in ball bearing systems. The presented method generalizes and improves upon earlier developed sparse reconstruction techniques, allowing for detecting multiple fault modes. The measured signal is corrupted with additive and multiplicative noise, yielding a signal that is highly erratic. Fortunately, the damaged ball bearings give rise to strong periodical structures which may be exploited when forming the proposed detector. Numerical simulations illustrate the preferred performance of the proposed method.",

keywords = "ADMM, Ball bearing systems, Convex optimization, Sparse reconstruction",

author = "Maria Juhlin and Johan Sw{\"a}rd and Marius Pesavento and Andreas Jakobsson",

year = "2018",

month = nov,

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doi = "10.23919/EUSIPCO.2018.8552950",

language = "English",

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pages = "2330--2334",

booktitle = "2018 26th European Signal Processing Conference, EUSIPCO 2018",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

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note = "26th European Signal Processing Conference, EUSIPCO 2018 ; Conference date: 03-09-2018 Through 07-09-2018",

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Juhlin, M, Swärd, J, Pesavento, M & Jakobsson, A 2018, Estimating faults modes in ball bearing machinery using a sparse reconstruction framework. in 2018 26th European Signal Processing Conference, EUSIPCO 2018. vol. 2018-September, 8552950, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 2330-2334, 26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 2018/09/03. https://doi.org/10.23919/EUSIPCO.2018.8552950

Estimating faults modes in ball bearing machinery using a sparse reconstruction framework. / Juhlin, Maria; Swärd, Johan; Pesavento, Marius et al.
2018 26th European Signal Processing Conference, EUSIPCO 2018. Vol. 2018-September IEEE - Institute of Electrical and Electronics Engineers Inc., 2018. p. 2330-2334 8552950.

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

TY - GEN

T1 - Estimating faults modes in ball bearing machinery using a sparse reconstruction framework

AU - Juhlin, Maria

AU - Swärd, Johan

AU - Pesavento, Marius

AU - Jakobsson, Andreas

PY - 2018/11/29

Y1 - 2018/11/29

N2 - In this work, we present a computationally efficient algorithm for estimating fault modes in ball bearing systems. The presented method generalizes and improves upon earlier developed sparse reconstruction techniques, allowing for detecting multiple fault modes. The measured signal is corrupted with additive and multiplicative noise, yielding a signal that is highly erratic. Fortunately, the damaged ball bearings give rise to strong periodical structures which may be exploited when forming the proposed detector. Numerical simulations illustrate the preferred performance of the proposed method.

AB - In this work, we present a computationally efficient algorithm for estimating fault modes in ball bearing systems. The presented method generalizes and improves upon earlier developed sparse reconstruction techniques, allowing for detecting multiple fault modes. The measured signal is corrupted with additive and multiplicative noise, yielding a signal that is highly erratic. Fortunately, the damaged ball bearings give rise to strong periodical structures which may be exploited when forming the proposed detector. Numerical simulations illustrate the preferred performance of the proposed method.

KW - ADMM

KW - Ball bearing systems

KW - Convex optimization

KW - Sparse reconstruction

U2 - 10.23919/EUSIPCO.2018.8552950

DO - 10.23919/EUSIPCO.2018.8552950

M3 - Paper in conference proceeding

AN - SCOPUS:85059807170

VL - 2018-September

SP - 2330

EP - 2334

BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th European Signal Processing Conference, EUSIPCO 2018

Y2 - 3 September 2018 through 7 September 2018

ER -

Estimating faults modes in ball bearing machinery using a sparse reconstruction framework

Abstract

Conference

Subject classification (UKÄ)

Free keywords

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