Abstract
The ensemble empirical mode decomposition (EEMD) was largely used in the diagnosis of the rotating machines but the EEMD shows a limitation with the detection of the impulses that are influenced by the presence of noise, the mode mixing, and the end effect. To detect the shocks due to the defect at an early stage, we propose to use the Time-frequency filtering (TFF) which was recently proposed by Flandrin. This method allows us to denoise the signal and gives promising results in the detection of the defects on machine elements.
In this work first, we show by simulated bearing signal the advantage of TFF compared to the EEMD in the detection of impulses. Then, we analyze real vibration bearing signals by using the two different time-frequency methods, ensemble empirical mode decomposition (EEMD) and Time-frequency filtering (TFF), and then we compare the results given by using the two methods separately and the results by a new method when we combine the two methods. The filtered modes are analyzed by calculation of the spectrum, which gives more information about the defect and allows us to read it frequencies and detect it at an early stage.
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Mahgoun, H., Ziani, R. (2019). Bearing Diagnostics Using Time-Frequency Filtering and EEMD. In: Felkaoui, A., Chaari, F., Haddar, M. (eds) Rotating Machinery and Signal Processing. SIGPROMD’2017 2017. Applied Condition Monitoring, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-96181-1_4
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DOI: https://doi.org/10.1007/978-3-319-96181-1_4
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