Bearing health diagnosed with a mobile phone: acoustic signal measurements can be used to test for structural faults in motors
Authors:
- Pawel Rzeszucinski,
- Maciej Orman,
- Cajetan T. Pinto,
- Agnieszka Tkaczyk,
- Maciej Sulowicz
Abstract
According to statistics, bearings are the most often failing elements of low voltage motors. At the same time diagnostics of rolling element bearings constitutes a well-established part of the rotating machinery condition monitoring domain. In many cases however the cost of installing a high-end accelerometer based bearing condition monitoring system, which is currently the most common approach in the industry, might be difficult to justify on non-critical machinery due to potentially long payback period on the investment. This text investigates the possibility of performing condition monitoring of rolling element bearings based on acoustic signals recorded by a standard, easily accessible mobile phone. The main difficulty in using mobile phone-embedded microphone for rotating machinery diagnostic purposes is the fact that the frequency response of the mobile phone microphone is very poor below 200Hz. The results presented in this text seem to indicate that with an appropriate signal processing approach, it is possible to indicate the presence of faults in the bearings.
- Record ID
- CUTc1e3ab13e5eb4d06a1423e5423272805
- Publication categories
- ;
- Author
- Journal series
- IEEE Industry Applications Magazine, ISSN 1077-2618, e-ISSN 1558-0598
- Issue year
- 2018
- Vol
- 24
- No
- 4
- Pages
- 17-23
- Other elements of collation
- fot.; wykr.; Bibliografia (na s.) - 23; Bibliografia (liczba pozycji) - 21; Oznaczenie streszczenia - Streszcz. ang.; Data udostępnienia on-line - 2018-04-20; Numeracja w czasopiśmie - Vol. 24, Iss. 4
- Substantive notes
- Informacja o projekcie podana przez autora, nie występuje na publikacji: E-2/664/2017/DS - Metody i algorytmy monitoringu i diagnostyki maszyn
- Keywords in English
- rolling element bearing, condition monitoring, spectral kurtosis, Hilbert transform, bispectrum
- DOI
- DOI:10.1109/MIAS.2017.2740463 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/8344408/ Opening in a new tab
- Related project
- Metody i algorytmy monitoringu i diagnostyki maszyn elektrycznych. . Project leader at PK: , ,
Działalność statutowa - Language
- eng (en) English
- Score (nominal)
- 20
- Publication indicators
- Citation count
- 24
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTc1e3ab13e5eb4d06a1423e5423272805/
- URN
urn:pkr-prod:CUTc1e3ab13e5eb4d06a1423e5423272805
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.