A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis
- Publisher:
- ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
- Publication Type:
- Journal Article
- Citation:
- Mechanical Systems and Signal Processing, 2022, 164
- Issue Date:
- 2022-02-01
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|---|---|---|---|---|---|
| 1-s2.0-S0888327021005896-main.pdf | 7.07 MB |
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Being an effective methodology to adaptatively decompose a multi-component signal into a series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited bandwidth, the variational mode decomposition (VMD) has received increasing attention in the diagnosis of rolling element bearings. In implementing VMD, an optimal determination of decomposition parameters, including the mode number and bandwidth control parameter, is the pivotal starting point. However, in practical engineering, heavy background noise, abnormal impulses and vibration interferences from other internal components, often bring great challenges in selecting mode number and bandwidth control parameter. These issues may lead to the performance degradation of VMD for bearing fault diagnosis. Therefore, a fault information-guided VMD (FIVMD) method is proposed in this paper for extracting the weak bearing repetitive transient. To minimize the effects of background noise and/or interferences from other components, two nested statistical models based on the fault cyclic information, incorporated with the statistical threshold at a specific significance level, are used to approximately determine the mode number. Then the ratio of fault characteristic amplitude (RFCA) is defined and utilized to identify the optimal bandwidth control parameter, through which the maximum fault information is extracted. Finally, comparisons with the original VMD, empirical mode decomposition (EMD) and local mean decomposition (LMD) are conducted using both simulation and experimental datasets. Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.
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