期刊
IEEE ACCESS
卷 9, 期 -, 页码 42397-42408出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3065825
关键词
Fault diagnosis; Vibrations; Time-frequency analysis; Computational modeling; Transforms; Feature extraction; Transient analysis; Fault diagnosis; multiple iterations; multiple transient extracting transform; time-frequency analysis
资金
- National Natural Science Foundation of China [51875032]
- Special Funds for Basic Scientic Research Operation Fees of Beijing-afliated universities [X20061]
- Open Research Fund Program of Beijing Engineering Research Center of Monitoring for Construction Safety and Postgraduate Innovation Project of Beijing University of Civil Engineering and Architecture [PG2020091, PG2020089]
A multiple transient extracting transform has been proposed in this study to effectively detect transient information in signals and achieve a more concentrated time-frequency representation. The results from numerical simulation and practical vibration signal analysis demonstrate the effectiveness of this method in transient feature extraction compared to other advanced time-frequency methods.
Transient impulsive signal is usually related with the bearing or gear local defect. It is very difficult to extract those multi-transient features due to the non-stationary of the corresponding vibration signals of rotating machinery. Time-frequency analysis is a suitable tool for analyzing non-stationary signals. A multiple transient extracting transform has been proposed in this work, which can not only effectively detect the multiple transient information in the signal, but also achieve a more concentrated time-frequency representation. The results of numerical simulation show the effectiveness of this proposed method. The proposed multi-transient extracting transform can better locate the transient features and has a lower time-consuming and better noise robustness, compared with the traditional time-frequency analysis methods. Finally, multi-transient extraction algorithm is utilized to analyze practical bearing vibration signals. It has been well demonstrated that the proposed method is more effective than other advanced time-frequency methods in the field of transient feature extraction.
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