Estimating faults modes in ball bearing machinery using a sparse reconstruction framework
(2018) 26th European Signal Processing Conference, EUSIPCO 2018 2018-September. p.2330-2334- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/08bfdf86-625e-43e3-97ec-e4d7c3ddb122
- author
- Juhlin, Maria LU ; Swärd, Johan LU ; Pesavento, Marius and Jakobsson, Andreas LU
- organization
- publishing date
- 2018-11-29
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- ADMM, Ball bearing systems, Convex optimization, Sparse reconstruction
- host publication
- 2018 26th European Signal Processing Conference, EUSIPCO 2018
- volume
- 2018-September
- article number
- 8552950
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 26th European Signal Processing Conference, EUSIPCO 2018
- conference location
- Rome, Italy
- conference dates
- 2018-09-03 - 2018-09-07
- external identifiers
-
- scopus:85059807170
- ISBN
- 9789082797015
- DOI
- 10.23919/EUSIPCO.2018.8552950
- language
- English
- LU publication?
- yes
- id
- 08bfdf86-625e-43e3-97ec-e4d7c3ddb122
- date added to LUP
- 2019-01-24 11:20:52
- date last changed
- 2022-01-31 17:00:25
@inproceedings{08bfdf86-625e-43e3-97ec-e4d7c3ddb122, abstract = {{<p>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.</p>}}, author = {{Juhlin, Maria and Swärd, Johan and Pesavento, Marius and Jakobsson, Andreas}}, booktitle = {{2018 26th European Signal Processing Conference, EUSIPCO 2018}}, isbn = {{9789082797015}}, keywords = {{ADMM; Ball bearing systems; Convex optimization; Sparse reconstruction}}, language = {{eng}}, month = {{11}}, pages = {{2330--2334}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Estimating faults modes in ball bearing machinery using a sparse reconstruction framework}}, url = {{http://dx.doi.org/10.23919/EUSIPCO.2018.8552950}}, doi = {{10.23919/EUSIPCO.2018.8552950}}, volume = {{2018-September}}, year = {{2018}}, }