Bearing defect and misalignment diagnostics using local regularity and sparse frequency analysis
Nissilä, Juhani; Laurila, Jouni; Ruotsalainen, Keijo; Liedes, Toni (2022-03-31)
Nissilä, J., Laurila, J., Ruotsalainen, K., & Liedes, T. (2022). Bearing defect and misalignment diagnostics using local regularity and sparse frequency analysis. In: Proceedings of The First SIMS EUROSIM Conference on Simulation and Modelling, SIMS EUROSIM 2021, and 62nd SIMS Conference on Simulation and Modelling SIMS 2021, September 21-23, Virtual Conference, Finland, pp. 171–179, https://doi.org/10.3384/ecp21185171
© 2022 Juhani Nissilä, Jouni Laurila, Keijo Ruotsalainen, Toni Liedes. This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022050332200
Tiivistelmä
Abstract
A local regularity signal can be estimated from a vibration measurement with the help of the continuous wavelet transform (CWT). The resulting local regularity signal contains a lot of diagnostic information about different faults states of a machine. It is also typically a sparse signal and thus not well suited for frequency analysis using the discrete Fourier transform (DFT). In this paper, the frequency analysis of the local regularity signal is performed using the Lomb-Scargle periodogram. Another possibility is to use the methods of compressed sensing. Vibration measurements from different fault states from test rigs are utilized in validating the proposed method and comparing it with other methods. The induced fault conditions include a bearing inner ring defect and misalignment of a claw clutch. The results are compared to more traditional spectra calculated directly from the vibration measurement, such as the spectrum of the squared envelope.
Kokoelmat
- Avoin saatavuus [31995]