Abstract
Fault detection of rotating machinery under heavy noise background, is a significant but difficult issue, and traditional fault detection approaches are difficult to apply. To address this problem, a novel approach that combines variational mode decomposition (VMD), L-Kurtosis and random decrement technique (RDT) is proposed, which procedures are summarized as follows. First, the raw vibration signal collected from the rotating component is decomposed using VMD into a set of intrinsic mode functions (IMFs), and the noise components can be separated from the raw signal. Second, the L-Kurtosis indicator is introduced to solve the problem that the fault information is difficult to track, and the optimal intrinsic mode function (IMF) can be determined according to the maximum L-Kurtosis value. Then, RDT is further employed to purify the optimal IMF to eliminate the other unknown interference sources. Finally, a Hilbert envelope spectrum analysis is used for detecting the fault type. In order to validate the proposed approach, the numerical simulations and real experimental investigations about rolling element bearing and gear are conducted. The results illustrate that the proposed approach can effectively detect faults of rotating components.
Highlights
Rolling element bearing and gear are the key components used in modern rotating machinery and play the increasingly important role
Zheng et al [16] optimized variational mode decomposition (VMD) technique and applied it to detect faults in rotating machinery, and the results shows that the optimized technique can extract the knocking abnormal noise component effectively
The non-stationary signal decomposition capability of VMD is explored in fault detection
Summary
Rolling element bearing and gear are the key components used in modern rotating machinery and play the increasingly important role. Du et al [18] proposed a novel technique named fractional iterative variational mode decomposition based on VMD, and the result verify its obvious advantage in processing the noisy signals and even signals containing weak components. Yang et al [19] combined VMD and phase space parallel factor analysis technique, and the result indicate that the combination has good performance in detecting weak fault signal of the rotating components. VMD is employed to decompose the vibration signal into a set of IMFs, L-Kurtosis is introduced to select the optimal IMF, RDT is applied to further extract the purified signal containing the faulty information, which is demodulated using a Hilbert envelope analysis to extract the faulty feature frequency.
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