Riku-Pekka Nikula, Konsta Karioja, Mika Pylvänäinen, Kauko Leiviskä, Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features, Mechanical Systems and Signal Processing, Volume 138, 2020, 106572, ISSN 0888-3270, https://doi.org/10.1016/j.ymssp.2019.106572
Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features
|Author:||Nikula, Riku-Pekka1; Karioja, Konsta2; Pylvänäinen, Mika2;|
1Control Engineering, Environmental and Chemical Engineering, Faculty of Technology, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland
2Intelligent Machines and Systems, Faculty of Technology, University of Oulu, P.O. Box 4200, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 4.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202001142040
|Publish Date:|| 2021-12-28
This study is focused on the application of automated techniques on low-speed bearing diagnostics. The diagnosis in low-speed conditions is hampered by the long periods between defect-related impulses and the high level of noise relative to the magnitude of the impulses. To detect a localised defect in such conditions, a new approach that uses vibration signals and information on the bearing defect frequencies is proposed. At first, the vibration signal is filtered in a specific frequency range to enable the detection of the impulses hidden in the signal. The filtered signal is then segmented into short time windows, the length of which are selected based on the bearing defect frequencies. Statistical time domain features are calculated from these windows to amplify and compress the impulses inflicted by the defect. Then, a criterion based on the autocorrelation values of specific time lags is calculated. An exhaustive search procedure is used to determine the frequency band for signal filtering and to select the statistical feature, which together maximises the proposed criterion. The highest value of the criterion is finally compared with the corresponding value from the baseline condition to detect the localised defect. The proposed technique is demonstrated on simulated signals, and validated based on the vibration signals from laboratory tests with undamaged, slightly damaged and severely damaged rolling elements in a rolling element bearing. Different conditions with shaft speeds from 20 to 80 rpm were studied in the laboratory tests. The proposed technique was compared with automated envelope spectrum diagnosis approaches based on the peak ratio and peak-to-median indicators and the fast kurtogram. The results reveal that the criterion based on autocorrelation gave defect indications associated with the correct type of defect in various circumstances while the tested envelope spectrum approaches were prone to induce an incorrect conclusion. Moreover, the results indicate that the approach could be used successfully on signals with a length that includes relatively few defect periods or impulses. The approach requires a high sampling rate relative to the defect frequencies, which may limit its suitability for the higher shaft speeds.
Mechanical systems and signal processing
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
214 Mechanical engineering
© 2019 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.